15 PhD positions: Development of One Chemistry: Unified and interpretable deep neural networks model for drug discovery

Helmholtz Zentrum München

Neuherberg near Munich, Germany

 

Expires on April 18, 2021

 

 

Advanced machine learning for Innovative Drug Discovery (AIDD) Project (http://ai-dd.eu)

Area of research:

Machine learning is changing our society, as exemplified by speech and image recognition appli­cations. Also the life sciences change rapidly through the use of arti­ficial intelli­gence, and it is expected that fields like drug develop­ment can take advantage of machine learning. The main goal of the AIDD project is to train and prepare the next generation of scientists who need to have skills in both machine learning and drug discovery and will, after graduating, be able to contribute to speed up the drug develop­ment process. The European Marie Skłodowska-Curie Innovative Training Network funds the AIDD project that brings together twelve academic partners (Helmholtz Zentrum München (coordinator), Germany; Aalto University, Finland; Freie Universität Berlin, Germany; Katholieke Universiteit Leuven, Belgium; Johannes Kepler Universität Linz, Austria; The Swiss AI Lab IDSIA, Switzerland; TU Dortmund, Germany; Universiteit Leiden, Netherlands; Université du Luxembourg, Luxembourg; University of Vienna, Austria; Universitat Pompeu Fabra, Spain and Vancouver Prostate Center, University of British Columbia, Canada) as well as four industrial partners (AstraZeneca, Sweden; Bayer Aktiengesellschaft, Germany; Janssen Pharmaceutica NV, Belgium and Enamine Limited Liability Company, Ukraine).

The AIDD network offers 15 PhD fellow­ships. The employed fellows will be super­vised by academics who have solid technical expertise and have contributed to some of the funda­mental AI algorithms which are used billions of times each day in the world, and by machine learning scientists working at pharma­ceutical companies. The developed methods by the fellows will contribute to an inte­grated “One Chemistry” model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehen­sive, structured training through a well-elaborated Curriculum, online courses, and six schools.

Each fellow will perform research 1.5 years at an academic partner and 1.5 years at an indus­trial partner.

For more information, visit http://ai-dd.eu

15 PhD positions: Development of One Chemistry:

Unified and interpretable deep neural networks model

for drug discovery

Deadline for applications: 18 April 2021

15 Early-stage Researcher Positions:

ESR1: One Chemistry: Unified and interpretable deep neural networks model for drug discovery

Academic Host: HMGU, Industrial Host: AstraZeneca

ESR2: One Chemistry: Robust learning of modular AI systems for the molecular generation, chemical reactions, and synthesis

Academic Host: LINZ, Industrial Host: AstraZeneca

ESR3: Prediction of chemical synthesis using NLP models

Academic Host: HMGU, Industrial Host: Janssen

ESR4: Prediction of yield and rates of chemical reactions

Industrial Host: Enamine, Academic Host: HMGU

ESR5: Reactivity for retrosynthesis steps by combining quantum mechanics and machine learning

Academic Host: UPF, Industrial Host: Bayer

ESR6: Integrating microscopy images from different sources to inform the compound design

Academic Host: TUDO, Industrial Host: Janssen

ESR7: Fast and scalable multi-objective synthesis route optimization

Academic Host: ULEI, Industrial Host: Bayer

ESR8: Learning Representation for Molecules from Chemical Structures and Microscopy Image

Academic Host: LINZ, Industrial Host: Janssen

ESR9: Improve drug design with human-assisted AI

Industrial Host: AstraZeneca, Academic Host: AALTO

ESR10: Improved uncertainty quantification of drug-target predictions through the utilization of auxiliary data

Academic Host: KUL, Industrial Host: AstraZeneca

ESR11: Machine learning models for the identification of compounds likely to interfere with biological assays

Industrial Host: Bayer, Academic Host: UNIVIE

ESR12: Prediction of outcome of chemical reactions using new neural network architectures

Academic Host: SUPSI, Industrial Host: Bayer

ESR13: Quantum machine learning for reactivity

Academic Host: ULUX, Industrial Host: Janssen

ESR14: Decomposable latent representations for in-vivo toxicity prediction

Academic Host: AALTO, Industrial Host: Janssen

ESR15: Deep Learning for protein simulation

Academic Host: FUB, Industrial Host: AstraZeneca

Essential Skills and Experience

• • Master’s degree in Computer Science, Cheminformatics, Bioinformatics or equivalent subject
  • Courses in machine learning
  • Courses in programming

 

Desired skills

• • Experience of software engineering
  • Proven experience of Python programming
  • Experience of deep learning libraries for instance TensorFlow or PyTorch)
  • Experience with libraries such as RDKit or scikit-learn would be of advantage
  • Good proficiency in modern software development tools, such as git
  • Courses in drug development

 

The ideal candidate will also demons­trate enthusiasm for propelling scientific questions with a positive and problem-solving attitude and the willing­ness to under­take complex analysis tasks in a timely fashion. Excellent English is required, both spoken and written, and the ability to work effec­tively both sepa­rately and in cross-functional teams. We also believe that you enjoy teamwork, have a collabo­rative nature and will be an encouraging colleague to all.

Benefits:

Marie Skłodowska-Curie funding offers attrac­tive salaries. Net salary is subject to country-specific deductions as well as depending on indivi­dual factors such as family allowance.

Eligibility criteria

Early-Stage Researchers (ESRs) shall, at the time of recruit­ment by the host organi­zation, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree.

At the time of recruitment by the host organi­zation, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organi­zation for more than twelve months in the three years immediately prior to the reference date. Compulsory national service and/or short stays such as holidays are not taken into account. As far as inter­national European interest organi­zations or inter­national organi­zations are concerned, this rule does not apply to the hosting of eligible researchers. However, the appointed researcher must not have spent more than twelve months in the three years immediately prior to their recruit­ment at the host organi­zation.

Eligibility and Mobility Rules are defined only at the first employment.

How to apply

1. 1. Prepare your profile and provide sufficient details about your educational back­ground and work experience, proofs of your educational degree (or expected time until you obtain your degree), your CV, and a cover letter outlining your motivation for applying for the position.
   2. Submit your application to recruit@ai-dd.eu before the deadline of April 18st, 2021 (the screening will start immediately; do not wait until the deadline to submit your appli­cation). Indicate ESR number in the title of the letter.

 

Further information is available on http://ai-dd.eu/esr-positions

click below to apply

https://www.nature.com/naturecareers/job/15-phd-positions-development-of-one-chemistry-unified-and-interpretable-deep-neural-networks-model-for-drug-discovery-helmholtz-zentrum-munchen-german-research-center-for-environmental-health-hmgu-737900