Data Science Postdoc in Machine Learning assisted identification of biological pesticide replacements for sustainable agriculture

torsdag 22 apr 21

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Frist 17. maj 2021
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As a Data Scientist joining this project, you will get the opportunity to use your Machine Learning and Data Engineering skills to help solve the pressing real-world problem of finding biological replacements of chemical pesticides to promote a more sustainable agriculture. You’ll be working with large volumes of data, ranging from images, DNA sequences, mass-spectra, and other forms of complex biological data. You will join a startup-like research environment, a machine park of robotics equipment, and a highly multi-disciplinary team of microbiologists, chemists, fermentation and lab automation engineers, that collaboratively work towards this common goal.

The project
The postdoc position is part of the Smarter AgroBiological Screening (SABS) project, which will develop new biological antifungal agents for crop protection, to enable the reduction of chemical fungicides usage. The project is an industry-university collaboration involving a large team at DTU Bioengineering, to build-up a set of high-throughput automated assays to screen DTU's collection of microorganisms and advance most promising strains towards in-planta testing. Artificial intelligence methodologies will be used to find predictive patterns in the collected data and enable most effective selection and optimization process. The SABS project is funded by the Innovation Fund Denmark and is located at DTU Bioengineering - Department of Biotechnology and Biomedicine.

The problem
Presently, the state-of-the-art approach of brute force screening of biological agents is simply too costly and time consuming, making it challenging to compete with existing chemical-based pesticides. So, innovative ideas and methodologies are needed to navigate this vast search space effectively. From a Machine Learning perspective, this problem boils down to a scenario of abundant unlabeled data (vast number of microorganisms are available for screening) that are costly to label experimentally (“Is this organism a crop protective agent?”). For this scenario, approaches such as Active Learning and Bayesian Optimization can be utilized to facilitate a cost-effective generation of data points.

Responsibilities and qualifications
As part of a multi-disciplinary team, your primary responsibility will be to develop the Machine Learning based experimental design pipeline to significantly reduce the vast search space of available microorganisms through iterative and model-guided experimentation using Active Learning, Bayesian Optimization, and other suitable approaches.

Tasks you will carry out for this purpose include:

  • Develop and validate predictive Machine Learning models based on iteratively generated biological data.
  • Utilize and communicate model predictions to determine next iteration of most informative experiments.
  • Develop data management solutions and guide experimentalists in systematic and automated data capture.
  • Support other team members in basic data processing tasks such as time series analysis and image processing.

As a formal qualification, you must hold a PhD degree (or equivalent) in Data Science or a related field (Bioinformatics, Computational Biology, Statistics, Applied Mathematics, Computer Science etc.).

Furthermore, you must have the following qualifications and skills:

  • Proficient in using the Python Data Science stack (NumPy, SciPy, Pandas, Scikit-learn, etc.) and visualization libraries (altair, plotly, matlotlib, etc.).
  • Experience with Deep Learning and at least one corresponding framework (PyTorch, Tensorflow, etc.).
  • Comfortable in modeling complex data by communicating with domain experts.
  • Proficient with basic back-end development using relational databases, ORMs etc. preferably in Python.
  • Good coding practices like test-driven development, version control with git, code reviews etc.

Preferred additional qualifications include:

  • Real-world project experience with Active Learning and/or Bayesian Optimization for experimental design is a plus.
  • Experience with image processing and analysis using skimage, opencv and/or CNNs is a plus.
  • Experience with Airflow (or similar) for managing data pipelines and ETL workflows is a plus.
  • Experience with cloud computing, Docker, Kubernetes is a plus.
  • Experience with using NoSQL/graph databases is a plus.
  • Bioinformatics experience with microbial genome sequence data processing (assembly and annotation) is a plus.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and terms of employment
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.

The period of employment is 2.5 years from July 1st, 2021.

You can read more about career paths at DTU here.

Further information
Further information may be obtained from Associate Professor Nikolaus Sonnenschein ( Website:

You can read more about DTU Bioengineering at

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.

Application procedure
Your complete online application must be submitted no later than 17 May 2021 (Danish time) - but please submit early as we will start a preliminary review and interview process.

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • Application (cover letter)
  • CV
  • Academic Diplomas (MSc/PhD)
  • List of publications

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

The Department of Biotechnology and Biomedicine (DTU Bioengineering) conducts research, educates future bio-engineers, provides scientific advice and innovate within the areas of microbiology, biochemistry, biotechnology and biomedicine. The research at DTU Bioengineering is at the highest international level and focuses on the societal and scientific challenges within the field. Research is conducted within three main areas: Microbial ecology and physiology, Industrial biotechnology and cell factories, and Biomedicine and health. The department has extensive collaboration with national and international research units and industries. DTU Bioengineering has approx. 160 employees, of which 2/3 are scientific staff. The department is located at DTU Lyngby Campus.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 12,900 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup and in Sisimiut in Greenland.