Postdoctoral research position to develop machine-learning methods for data anonymization and evaluation of the anonymized biomedical data based on data quality and risk for re-identification is available for applicants with doctoral degree at the Departments of Computing Sciences, Physics, and ...

We are seeking outstanding candidates with strong analytical and problem-solving skills, who are strong in written and oral communication (in English) and have documented experience in machine learning (in particular deep learning) and bioinformatics. Expertise in handling and understanding protein mass spectrometry data is an advantage, but not a requirement. The successful candidate will participate in independent research projects and assist in the supervision of undergraduate students. The selected candidate will develop a proof-of-concept framework for detecting antibody-derived peptide signatures in proteomics datasets. More specifically, the research project entails the analysis of millions of B-cell receptor sequences by machine learning to determine disease-specific antibody peptides that can be detected in proteomics datasets.

The Post-doc will spend 80% of their time on research and 20% of their time in related educational activities. We do not respond to applications that are sent to us in a different way. Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files. We look forward to your application and will screen it as soon as we have received it.

The main goal of the Digital Future Farm project is to create a "digital twin" of arable and dairy farms able to mimic farm process interactions, and allow for the exploration of interventions. To achieve this aim, the project will pull together multifaceted, multiscale data (from remote sensing, IoT sensors, farm-management systems) and process-based models in a common infrastructure. Part of the project ambition is to investigate to what extent machine learning and deep learning models can improve decision-making at a farm-level. Using real and simulated data you will be challenged to design and implement machine learning pipelines for estimating farm yields and nutrients application, and evaluate their performance using the Digital Future Farm project case studies. Your overarching ambition will be a methodological contribution in the area of machine learning applications for farm-level decision making.

The Matthias Mann lab at the Max Planck Institute of Biochemistry is a leader in the field of mass spectrometry-based proteomics and has pushed the development and application of this technology for over two decades. The Fabian Theis lab at the Helmholtz Center Munich has a long-standing reputation for pioneering machine learning and AI methods in molecular biology, in particular on single-cell genomics and microscopy. They have recently joined forces in a project to develop novel deep learning techniques for peptide analysis and predictions on multiple levels, which potentially revolutionizes proteomic workflows in terms of accuracy and efficiency. Together the Theis and Mann labs are looking for two highly motivated postdoc candidates for working in a team that will combine newest developments in both Machine Learning and proteomics. This technology will be applied to the diagnosis and prognosis of disease on the basis of MS-based proteomics.

The Institute of Formal and Applied Linguistics (UFAL) is seeking a candidate for a one-year post-doc position in the area of neural machine translation (NMT). The exact topic will be determined based on the candidate's interests, e.g. A PhD degree in computational linguistic, artificial intelligence or a related field is required. Experience with neural MT, Linux and cluster environment (SGE), and/or general deep learning and GPU computation is a bonus.

The Machine Learning (AppStat) group of the Linear Accelerator Laboratory (LAL) is seeking a postdoctoral researcher for working at the interface of machine learning and experimental high-energy particle physics. The researcher will work with the dynamic interdisciplinary group which organized the HiggsML challenge, including Cecile Germain and Isabelle Guyon (LRI), and Balázs Kégl and David Rousseau (LAL) Some of the ongoing themes are deep learning for next-generation high-resolution pixel calorimeters, including the systematic uncertainties in training ML models for discovery, and budgeted learning for real-time triggers. All themes include the development of state-of-the-art ML solutions that can make a real difference in both the design and in the data analysis phases of ongoing and future large-scale physics experiments (e.g., ATLAS, LHCb, the future ILC). The candidate will also be part of the Paris-Saclay Center of Data Science and will be expected to participate in the mission of the center through its activities (eg, RAMPs, software carpentry, thematic days). We are accepting candidates both from data science (ML, statistics, signal processing) and from high-energy physics, given that the candidate is ready to cross the disciplinary aisles.