Recruitment

Chemical molecules are classically described as undirected graphs of bounded degree, with nodes representing atoms and edges representing bonds. This implies that chemical reactions can be well described by graph transformation formalisms and methods. A computational framework for executing these methods has been developed, but there is a challenging need to expand it to allow for more detailed information encoded in attributed graphs. This will require the generalization of established algorithmic approaches. One example of this is the canonicalization of molecular graphs that include stereochemical information – highly efficient canonicalization algorithms are key when huge chemical spaces are to be generated by graph transformation based methods, but current algorithms do not incorporate stereochemical information.

The goal of this PhD project is to combine state-of-the art approaches from graph theory, algorithms, and algorithm engineering in order to develop the basis of ground-breaking computational methods applicable to the analysis of large networks of chemical reactions.

We are seeking an excellent and highly motivated individual with a MSc degree in computer science or a related subject*. The ideal candidate has familiarity with one or more of the following areas: algorithmics, category theory, graph theory, graph transformation, algorithm engineering. Proven competences in programming and ease with formal thinking are a necessity.

*Applications from students that passed master’s degree examinations that correspond to 60 of 120 ECTS credits are possible.

Principal Supervisors: Daniel Merkle, Jakob Lykke Andersen, Peter Stadler
Hosting sites: University of Southern Denmark (SDU) and Leipzig University (LU)
Planned Secondments: BASF (DE)

Application is currently closed.

The application of techniques from group theory, graph theory, concurrency theory (e.g. Petri Nets) and related areas to the modelling of atom tracking in chemistry has a huge potential. Atom tracking is used in chemistry to understand the underlying reactions and interactions of chemical or biological systems, usually by using isotopic labelling methods. In a typical isotopic labelling experiment, one or several atoms of some molecule of the chemical system under examination are replaced by an isotopic equivalent. These compounds are then introduced to the system, and the resulting product compounds are examined, e.g., by mass spectrometry. From a theoretical perspective, developing a formal framework for tracking atoms through reactions is an important step towards understanding the possible behaviours of a chemical or biological system. Such a framework must include a formal definition of an event trace which defines where a specific labelled atom can end up. In addition, an efficient computation of symmetries in (molecular) graphs is key.

The goal of this project is to apply and expand on established techniques from graph theory, group theory, semigroup theory, or concurrency theory in order to develop such a framework, thereby extending our abilities to reason about biochemical systems. The student will have the opportunity to apply the methods developed in an industrial setting via a 3 months research stay at BASF SE.

We are seeking an excellent and highly motivated individual with a MSc degree in computer science, applied mathematics, computational chemistry, or a related subject*. The ideal candidate has familiarity with one or more of the following areas: discrete structures, graph theory, group theory, concurrency theory, and should be interested in interdisciplinary studies.

*Applications from students that passed master’s degree examinations that correspond to 60 of 120 ECTS credits are possible.

Principal Supervisors: Daniel Merkle, Rolf Fagerberg, Peter Dittrich
Hosting sites: University of Southern Denmark (SDU) and FSU-Jena
Planned Secondments: BASF

Application is closed.

Systems theory defines a chemical reaction system as transformations of abstract entities i.e only the stoichiometric relations between the entities. Rule-based approaches on the other hand focus on the rearrangements of chemical bonds and therefore require that the entities have explicit internal structure. This imposes constraints on the stoichiometry and vice versa the system level constrains the molecules (entities) given the rules and the rules given the molecules. The aim is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete “chemistry” defined as a pair of molecules and rules. 

Applications will lie in the area of the design of enzyme cascades. 

The PhD candidate will study abstract reaction networks with respect to their realizability with concrete chemistry. For example, given a metabolic network is there an alternative chemistry that produces the same stoichiometric network? Naturally, this type of problem can be formalised in the framework of SMT (Satisfiability Modulo Theory). The PhD project involves the development of a theory and accompanying algorithms to tackle such problems.

We are seeking an excellent and highly motivated individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics, graph theory, satisfiability problems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity.

NOTE: The MSc degree must be completed prior to beginning of employment at Leipzig University. A BSc degree and extra qualifications in total equivalent to a 5-year program are also acceptable.

Principal Supervisors: Peter Stadler, Christoph Flamm
Hosting sites: Leipzig University (LU) and University of Vienna (UV)
Planned Secondments: TU Vienna

Application is currently closed.

Chemical reactions can be understood as transformations based on a limited set of rules that capture the essence of chemical reactions, namely the change of patterns of chemical bonds. These rules roughly correspond to the “named reactions” central in chemical synthesis. The project aims to develop a collection of methods to infer the rules of chemical transformations from large data sets of chemical reactions. Chemical reactions are performed under specific conditions, such as temperature and solvent. 

The PhD candidate will build upon methods for graph comparison as well as more traditional machine learning methods familiar from QSAR to develop algorithms for the inference of reaction rules for large-scale data sets of chemical reactions. Moreover, we will develop learning-based tools to determine the reaction conditions under which rules are applicable, i.e., under which a certain type of chemical reactions can be performed. The project will involve the development of innovative methods and their implementation as well as applications to large-scale data.

We are seeking an excellent and highly motivated individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: machine learning, graph theory, optimization, statistical learning. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity.

NOTE: The MSc degree must be completed prior to beginning employment at Leipzig University. A BSc degree and extra qualifications in total equivalent to a 5-year program are also acceptable.

Principal Supervisors: Peter Stadler, Daniel Merkle
Hosting sites: Leipzig University (LU) and University of Southern Denmark (SDU)
Planned Secondments: TU Vienna

Application is closed.

Chemical transformations are governed by rules that comprise (a) a reaction mechanism that describes the change in molecular structures and (b) a set of constraints that restricts reactions to particular solvents, pH ranges, and other physical parameters that collectively form the reaction conditions. The project aims to use machine learning to infer such transformations.

The PhD candidate will develop novel machine learning algorithms to infer constraints on reaction conditions based on state-of-the-art machine learning and data science techniques, including kernels for structured data in Hilbert and/or Kreı̆n spaces. Phrased as a classical decision problem, we will ask whether a set of molecules can react according to a given mechanism under a given reaction condition. More generally, we will aim at predicting the mechanism and conditions for given sets of molecules. In machine learning terminology this corresponds to a structured output prediction problem for multi-instance data. Predicted individual reactions constitute parts of synthesis plans that will be studied as shorted path problems in the hypergraph of possible chemical reactions. 

We are seeking an excellent and highly motivated individual with a MSc degree in computer science, mathematics, or a related subject. The ideal candidate has familiarity with machine learning and one or more of the following subjects: optimization, graph theory, statistics. Proven competences in programming and formal thinking are a necessity. An interest in chemistry is beneficial.

NOTE: The MSc degree or equivalent qualification must be completed prior to beginning employment.

Principal Supervisors: Thomas Gärtner, Peter Stadler
Hosting sites: TU Wien (TUW) and Leipzig University (LU)
Planned Secondments: SDU

Application is closed.

Chemists often work with complex mixtures of reagents and can only detect if a particular molecule has been generated, but not how it was generated in the mixture. The question which sequence of reaction events (mechanism) resulted in the emergence of the observed molecule in the reaction mixture can be approached via computer simulation. Theoretical concepts and software implementations to describe the dynamics of rule based Chemistry at multiple scales and from multiple perspectives shall be developed within the project. A network free stochastic simulation engine for Chemistry specified in the double pushout formalism shall be implemented, alongside with a framework to analyse the stochastic simulations. For example automated causal reconstruction of reaction mechanisms from ensembles of trajectories, rare event sampling, or attractor and stability analysis based on chemical organisation theory shall be performed.

If you are a computer scientist, mathematician, physicist or computational biologist with a strong interest in chemical reactive systems and how to describe, simulate and analyse these discrete event systems, then we invite you to apply for this exciting PhD position in our team! 

The PhD project involves the development of theory, algorithms and software implementations. The PhD candidate will study discrete event systems in the form of rule based stochastic Chemistry. The ideal candidate has familiarity with one or more of the following areas: algorithmics, graph theory, stochastic systems, discrete optimization. Strong interests in chemistry as well as proven competences in programming and ease with formal thinking are a necessity.

Principal Supervisors: Peter Dittrich, Christoph Flamm
Hosting sites: FSU Jena (FSU) and University of Vienna (UV)
Planned Secondments: Harvard Medical School

Application is currently closed.

Most projects within TACsy are built on a powerful methodology which strikes a unique balance between chemical modelling accuracy and computational efficiency. The methodology lies in the intersection of algorithmic methods from computer science, graph theory and systems chemistry. On the lowest level of abstraction, graph transformation approaches can be used to model the movement of electrons that form and break bonds and change the charges of incident atoms. This level of abstraction allows for interfacing with well-established techniques from quantum chemistry (QC) and to compute reaction paths, energy profiles, and transition states. While computationally expensive, QC can be used to, e.g., accurately infer graph transformation rules verified by QC and likelihoods of the elementary events needed to parameterize stochastic simulations of chemical reactions. 

The goal of this PhD project is to integrate state-of-the-art graph transformation and QC approaches. You will work in close collaboration with BASF SE (12 months research stay) in order to apply the new approaches to mechanistic studies of industrially relevant reaction cascades. Within TACsy we will share forces to pave the road to a green superabsorber polymer, for which all carbon atoms originate from renewable sources.

We are seeking an excellent and highly motivated individual with either an MSc degree in computer science or an MSc degree in chemistry or physics (preferably with a focus on computational methods)*. The ideal candidate has familiarity with one or more of the following areas: algorithmics, graph theory, graph transformation, algorithm engineering (for CS students) or theoretical chemistry, quantum chemistry, semi-empirical methods, computational methods in quantum mechanics (for students of chemistry or physics). Proven competences in programming and ease with formal thinking are a necessity. Preexisting knowledge of quantum mechanical methods is a plus, but not a necessity.

*Applications from students that passed master’s degree examinations that correspond to 60 of 120 ECTS credits are possible.

Principal Supervisors: Daniel Merkle, Peter Stadler
Hosting sites: University of Southern Denmark (SDU) and Leipzig University (LU)
Planned Secondments: BASF (DE)

Application is closed.

Within TACsy we will develop novel theoretical techniques, algorithms, and tools to deal with the kinetics of chemical systems and we will evaluate these methods within a flagship application of industrial relevant reaction cascades. Discrete event-driven simulation tools will be key for this. Established formulations of the stochastic simulation algorithm often assume that a network is given at the outset. However, stochastic simulations can also be used as an explorative tool to find and study novel chemical behaviour in implicitly defined systems. This mode of operation is of particular importance for reactive chemical systems, where either the exact conditions that constrain the state space are unknown or the state space itself is combinatorially large or even unbound.

Within this project, discrete event-driven simulation tools will be implemented in collaboration with partners from the Harvard Medical School (HMS, first research stay, 3 months). The simulator will go beyond established ones by considering atom-level resolution and by, e.g., also exploiting symmetries in molecules. Within this project the student will also analyse the sequence of events of stochastic simulations. This will, among other things, allow us to argue about the causal structure of systems. In a later step the student will exploit the fact that a rule-based system will allow to automatically switch between levels of abstraction in a mathematically rigorous manner. The developed simulation framework will be applied in an industrial setting to analyse systems with stereospecific or stereoselective behaviour (BASF SE, second research stay, 3 months).

We are seeking an excellent and highly motivated individual with an MSc degree in computer science, applied mathematics, computational chemistry, or a related subject*. The ideal candidate has familiarity with one or more of the following areas: discrete structures, concurrency theory, stochastic simulations, or algorithmics, and should be interested in interdisciplinary studies. Proven competences in programming and ease with formal thinking are a necessity.

*Applications from students that passed master’s degree examinations that correspond to 60 of 120 ECTS credits are possible.

Principal Supervisors: Daniel Merkle, Peter Dittrich
Hosting sites: University of Southern Denmark (SDU) and FSU Jena
Planned Secondments: Harvard Medical School

Application is closed.

Many key questions and challenges in research, industry, and society involve large and complex Chemical Reaction Networks. Due to the size and combinatorial complexity of these systems, it is infeasible to manually analyse their properties and explore their design space. Within TACsy we will develop new approaches based on modelling chemical reactions via an expressive graph transformation language for which we have created an efficient computational framework that makes this language executable. Furthermore, our approaches allow an integration of well-established techniques from computational quantum chemistry.

TACsy includes several flagship projects which will showcase our approaches in a real-world setting. Examples of immense economical as well as environmental interest are polymer degradation pathways and the so-called dream reaction to create a green superabsorber polymer, for which all carbon atoms originate from renewable sources. Within this project the student will contribute to developing generic kinetic approaches for the catalytic synthesis. Furthermore the results will showcase the graph transformation approach to promote and disseminate consortium projects. Naturally, this PhD project will include a long research stay with one of our main industry partners (12 months with BASF SE).

We are seeking an excellent and highly motivated individual with either an MSc degree in computer science, an MSc degree in Applied Mathematics, or an MSc degree in chemistry or physics (preferably with a focus on computational methods)*. The ideal candidate has familiarity with one or more of the following areas: algorithmics, graph theory, graph transformation, algorithm engineering (for CS students) or theoretical chemistry, quantum chemistry, semi-empirical methods, computational methods in quantum mechanics (for students of chemistry or physics). Proven competences in programming and ease with formal thinking are a necessity. Preexisting knowledge of quantum mechanical methods is a plus, but not a necessity.

Principal Supervisors: Daniel Merkle, Peter Dittrich
Hosting sites: University of Southern Denmark (SDU) and FSU Jena
Planned Secondments: BASF

Application is closed.

Multi-enzymatic cascade reactions, i.e., the combination of several enzymatic transformations in one pot, have proven to have considerable advantages compared to classical synthesis. Such reactions, often called biocatalytic reaction cascades because they mimic Nature’s approach to synthesise chemical compounds, have unique properties. E.g., it is possible to directly reduce costs, to allow for overall reactions that would otherwise not be possible, and the concentration of harmful or unstable compounds can be kept to a minimum. Declarative approaches to solve design and optimization problems are well established in computer science: one does not aim at specifying how a solution is implemented, but one rather specifies what an overall goal should be and the solution is then automatically computed. Such approaches have an immense potential in general for chemistry, and more specifically for the design of multienzyme cascades: conceptually enzymes define graph transformation rules, which can be used to expand chemical spaces. The declarative approaches can subsequently be used to find efficient reaction cascades (i.e. sequences of graph transformation rules).    

In this project, the PhD candidate will focus on declarative approaches to solve multi-enzymatic cascade design problems. The design focus lies on local and global properties of the solutions such as allosteric regulation of enzymes, competitive inhibition of the active sites, cofactor recycling, switching between different operation modes, or overall autocatalytic behaviour. The candidate will develop such approaches and conduct extensive in-silico testing. We aim to compare and evaluate the computational tools developed in this project against wet-lab data produced in other projects of TACsy. The student will have a 3 month research stay with our industry partner (Fluigent) focusing on microfluidics and lab-on-a-chip technologies.

We are seeking an excellent and highly motivated individual with either an MSc degree in computer science, an MSc degree in Applied Mathematics, an MSc degree in chemistry, or a degree from a related field (preferably with a focus on computational methods)*. The ideal candidate has familiarity with one or more of the following areas: optimization, declarative approaches, algorithmics, graph theory, algorithm engineering, computational chemistry. Proven competences in programming and ease with formal thinking are a necessity.

Principal Supervisors: Christoph Flamm, Daniel Merkle
Hosting sites: University of Vienna (UV) and University of Southern Denmark (SDU)
Planned Secondments: Fluigent

Application is closed.

There is increasing interest in the production of chemicals from renewable bio-based feedstocks in the transition to a circular economy. Great advances have been made in the use of biological pathways to replace traditional chemical routes, for example, in the production of polylactic acid from lactic acid bacteria. However, there are limitations in exploiting living organisms and some processes remain uneconomical. Transformations may be achieved under wider conditions using non-natural cascade reactions involving multiple enzymes and efforts have been made to split reactions into small functional units, which can be recombined into production pipelines for various applications. 

The aim of this project is to develop computational methods for MEC modularization for the production of chemicals from biomass. The project will involve design of novel pathways for products such as bioplastics using computational techniques at University of Vienna, Austria and a 9 month secondment at the University of Sheffield, UK to develop microfluidics methods for screening relevant enzymatic reactions and “wet-lab debugging” approaches. You will also undergo a short secondment to Fluigent for training in microfluidics. 

We are seeking a PhD candidate that has or is expected to have a Masters degree in Chemical/Biochemical Engineering, Computer Science, Chemistry or related discipline at the time of recruitment. Some knowledge of enzyme kinetics, systems chemistry/biology or mathematical biology would be beneficial. Willingness to travel and work as part of an interdisciplinary team is essential!

Principal Supervisors: Christoph Flamm, Daniel Merkle, Annette Taylor
Hosting sites: University of Vienna (UV) and University of Southern Denmark (SDU)
Planned Secondments: University of Sheffield, Fluigent

Application is closed.

Upgrading of chemicals such as caffeine to more valuable products can be difficult to achieve using conventional synthetic methods, however strategies are emerging involving multi-enzyme cascades (MECs) and the exploitation of enzymes found in cell extracts has proved effective under certain conditions. The performance of these MECs could be greatly improved by augmenting the basic enzyme setup with salvation pathways that counteract degradation processes of important cofactors and co-substrates.  Computational methods allow us to explore the diversity of the MEC design space in-silico prior to wet-lab implementation and will thus hugely speed up the development process of productive MECs.

The aim of this project is to develop computational methods for the design of MECs for chemical upgrading with cofactor/co-substrate recycling. The project will involve the investigation of potential pathways using computational techniques at the University of Vienna and a 9 month secondment at the University of Sheffield to develop microfluidics methods for process optimization with encapsulated cell extracts. You will also undergo a short secondment to Fluigent for training in microfluidics. 

We are seeking a PhD candidate that has or is expected to have a Masters degree in Chemical/Biochemical Engineering, Computer Science, Chemistry or related discipline at the time of recruitment. Some knowledge of enzyme kinetics, systems chemistry/biology or mathematical biology would be beneficial. Willingness to travel and work as part of an interdisciplinary team is essential!

Principal Supervisors: Christoph Flamm, Daniel Merkle, Annette Taylor
Hosting sites: University of Vienna (UV) and University of Southern Denmark (SDU)
Planned Secondments: University of Sheffield, Fluigent

Application is open until 09 November: https://www.sdu.dk/da/service/ledige_stillinger/1215304

Several approaches developed within TACsy are based on a graph transformation language which is used to expand chemical spaces, similarly to how production rules in formal languages are used to derive words of a formal language. Both metabolic networks and the fragmentation space within a mass spectrometer can be modelled as a rule-based system. The latter has the advantage that the combinatorics is far less dramatic, as fragmentation splits (but not merges) chemical compounds. Finding fragmentation rules as graph transformation rules, however, is a challenge and requires automation. 

Within this project the PhD student will develop novel machine learning algorithms for automated rule inference in the mass spectrometer setting. For the subsequent spectrum prediction these results will be integrated with approaches that stochastically simulate the fragmentation process in a mass spectrometer. The PhD student will have a 12 month research stay with our industry partner Thermo Fisher Scientific, a world leader in instrumentation and mass spectrometry. 

In addition to developing novel algorithms for rule inference and mass spectrum prediction, the PhD student will have the chance to apply them in a flagship project of TACsy (in collaboration with our scientific partner EMBL): After modelling lipid metabolic networks as graph transformations, the computational framework developed will support functional studies of how the lipid metabolic networks of mouse embryonic stem cells are remodelled during cell fate acquisition. 

We are seeking an excellent and highly motivated individual with either an MSc degree in computer science, an MSc degree in Applied Mathematics, or an MSc degree in chemistry or physics (preferably with a focus on computational methods)*. The ideal candidate has familiarity with one or more of the following areas: machine learning, data mining, knowledge discovery, stochastic simulations, algorithmics, graph theory, graph transformation, algorithm engineering. Proven competences in programming and ease with formal thinking are a necessity. 

Principal Supervisors: Daniel Merkle, Rolf Fagerberg, Thomas Gärtner
Hosting sites: University of Southern Denmark (SDU) and TU Wien (TUW)
Planned Secondments: Thermo Fisher Scientific

Application is currently closed.

Are you a biologist with interest in learning and applying graph theory to interesting biological questions about cell identity and differentiation? Do you want to work at the interface between biology, biophysics and computer science? Then we invite you to apply for this exciting PhD position in our team!

The PhD candidate will use cell biology, biophysics and mass spectrometry approaches to study how regulation of lipid metabolism impinges on cell differentiation (at EMBL-SDU) and utilize graph theory to decipher the underlying metabolic networks at play in collaboration with computer scientists (at FSU-Jena). Expected results include establishing a TACsy-based framework for lipid metabolic flux analysis (collaborative effort) and to identify which lipid metabolic pathways and enzymes are involved in fate acquisition. Thus, we are looking for a PhD candidate that is interested in developing skills both for dry and wet lab work.

We are looking for candidates with the following profile:

MSc in Biology or similar lab experience after a BSc
Skills in programming are desired
Experience in tissue culture is desired
Team player
Good organizational and communication skills
Excellent command of English (C1 level)
Willingness to learn and be out of your comfort zone is essential

Principal Supervisors: Peter Dittrich, Alba Diz-Muñoz
Hosting sites:  FSU-Jena (FSU) and European Molecular Biology Laboratory (EMBL)
Planned Secondments: Thermo Fisher Scientific

Application is closed.

Multienzyme cascades (MECs)  provide an economic and environmentally friendly alternative to conventional catalysis, capable of complex chemical transformations in aqueous solution at mild pH and temperatures. However, the optimization of MECs is often limited by labor intensive manual variation of experimental conditions such as enzyme compatibility, buffer systems etc. Microfluidics devices can provide the means to miniaturize, parallelize and automate chemical and biological experiments, in order to rapidly find the conditions for maximal productivity.

The aim of this project is to design and implement a microfluidic platform for screening of selected multi-enzyme cascade reactions. You will work in close collaboration with computational scientists at the University of Vienna and microfluidics company Fluigent, in order to develop droplet microfluidic technology and suitable analytical methods for monitoring MECs in a highly parallel and automated fashion.

We are seeking a PhD candidate that has, or is expected to have by the time of appointment, a Master’s degree in Chemical/Biochemical Engineering, Computer Science, Chemistry or related discipline. Some knowledge of enzyme kinetics, systems chemistry/biology or microfluidics would be beneficial. Willingness to travel and work as part of an interdisciplinary team is essential!

Principal Supervisors: Annette Taylor, Christoph Flamm
Hosting sites: University of Sheffield

Application is closed.