PhD Position - Application of Machine Learning to Ranking Exhaustive Enumerations of Molecular Conformers

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Advertising division: IEK-8 - Troposphere

Reference number: 2022D-049

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You want to apply your data science knowledge to the basic research questions and societal challenges of our modern world? Our scientists in HDS-LEE address some of the most pressing issues of our time, such as energy transition, climate change and resource scarcity, brain function, drug design, identification of diseases at very early stages.

As Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), we aim to educate and train the next generation of data scientists during their doctoral thesis in close contact to domain-specific knowledge and research in three application domains: Life and medical science, earth science, energy systems and material science. Visit HDS-LEE at: https://www.hds-lee.de/

This position will be located at Forschungszentrum Jülich, Institute for Energy and Climate Research - Troposphere (IEK-8). Find more Information about the IEK-8 here: www.fz-juelich.de/iek/iek-8/EN/

We are looking to recruit a

PhD Position - Application of Machine Learning to Ranking Exhaustive Enumerations of Molecular Conformers

Your Job

Researchers at the IEK-8 investigate the chemical reactivity of organic molecules emitted to the atmosphere. To accurately predict the rate of reaction using theoretical methodologies, this requires the computationally expensive characterization of all molecular shapes. By joining forces with the Institute for Advanced Simulations (IAS-8), we aim to study and create machine learning techniques to fully enumerate the likely molecular geometries involved for both reactants and transition state, and rank them by their estimated relative energies. This information then allows for reduction in computational effort, as well as enabling hybrid approaches for subsets of the structures.

Your Tasks

You will investigate the applicability and performance of machine learning techniques to the prediction of molecular geometries, and their energybased ranking. This includes benchmarking the reduction of computational cost for the prediction of chemical reactivity, as well as demonstrating the general applicability of the approach based on relevant examples in various application domains.

The Project Includes

• Preparing and encoding training and validation data sets based on available quantum chemical data
• Implementing, applying, and benchmarking various machine learning techniques to the problem
• Adapting and extending promising machine learning techniques for more performant analysis
• Expanding the most promising techniques from smaller to more complex medium-sized molecules
• Demonstrating the applicability to reactions in atmospheric and/or combustion chemistry
  
  

Your Profile

• You have a high interest to apply your data science knowledge to molecular modelling
• M. Sc. degree in physics, mathematics, chemistry, meteorology, or a related field
• Experiences in data science, big data analyses, or deep learning methods are of great advantage
• Good knowledge in software development using Python, modern Fortran, or other languages
• Experiences in high performance computing (HPC) is of advantage
• Good skills in the spoken and written English language: TOEFL or equivalent evidence of English-speaking skills
• Outstanding organizational skills and the ability to work independently
• Very good cooperation and communication skills and ability to work as part of a team in an international and interdisciplinary environment
• A high level of scholarship as indicated, for example, by bachelor and master study transcripts and two reference letters
  
  

Our Offer

This position will be located at IEK-8, Forschungszentrum Jülich. We offer:

• Outstanding scientific and technical infrastructure – ideal conditions for successfully completing a doctoral degree
• A highly motivated group as well as an international and interdisciplinary working environment at one of Europe’s largest research establishments
• Continuous scientific mentoring by your scientific advisors
• Chance of participating in (international) conferences
• Unique HDS-LEE graduate school program
• Qualification that is highly welcome in industry
• Targeted services for international employees, e.g. through our International Advisory Service
• Further development of your personal strengths, e.g., via a comprehensive further training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/judocs
• You will be supervised by Prof. Dr. Astrid Kiendler-Scharr and Dr. Luc Vereecken.
  
  

Please Upload The Following Documents With Your Application

• Application letter explaining the motivation for the position and research interests
• Curriculum vitae
• Bachelor and master or diploma study transcripts
• English skills: TOEFL score or equivalent evidence of English-speaking skills (high school diploma “Abitur” with English as main school subject)
• Two letters of recommendation/ reference letters (alternatively you can provide the name and contact details of two referees)
  
  

The position is initially for a fixed term of 3 years. Pay in line with 100% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment („Christmas bonus“). Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at: www.fz-juelich.de/gp/Careers_Docs

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

You want to apply your data science knowledge to the basic research questions and societal challenges of our modern world? Our scientists in HDS-LEE address some of the most pressing issues of our time, such as energy transition, climate change and resource scarcity, brain function, drug design, identification of diseases at very early stages.

As Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE), we aim to educate and train the next generation of data scientists during their doctoral thesis in close contact to domain-specific knowledge and research in three application domains: Life and medical science, earth science, energy systems and material science. Visit HDS-LEE at: https://www.hds-lee.de/

This position will be located at Forschungszentrum Jülich, Institute for Energy and Climate Research - Troposphere (IEK-8). Find more Information about the IEK-8 here: www.fz-juelich.de/iek/iek-8/EN/

We are looking to recruit a

PhD Position - Application of Machine Learning to Ranking Exhaustive Enumerations of Molecular Conformers

Please Upload The Following Documents With Your Application

• Application letter explaining the motivation for the position and research interests
• Curriculum vitae
• Bachelor and master or diploma study transcripts
• English skills: TOEFL score or equivalent evidence of English-speaking skills (high school diploma “Abitur” with English as main school subject)
• Two letters of recommendation/ reference letters (alternatively you can provide the name and contact details of two referees)
  
  

The position is initially for a fixed term of 3 years. Pay in line with 100% of pay group 13 of the Collective Agreement for the Public Service (TVöD-Bund) and additionally 60 % of a monthly salary as special payment („Christmas bonus“). Further information on doctoral degrees at Forschungszentrum Jülich including our other locations is available at: www.fz-juelich.de/gp/Careers_Docs

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.