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.

Applicants must have earned a PhD in Artificial Intelligence, interaction design, human-computer interaction, or similar subjects relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist. The candidate is expected to have an overall interest in responsible AI concepts and methods, and expertise in participatory methods and interaction design, as demonstrate by publications and other scientific output. Proficiency in English, both spoken and written, is required. Ideal candidates are research driven, organized, and would like to work on challenging problems and innovative solutions.

Moving towards climate security, electric power systems are going through a major paradigm shift with wide integration of distributed energy resources, such as solar PV, wind power, energy storage and electric vehicles. However, today's grid cannot handle the voltage rise and fast voltage fluctuations from high penetration of renewables. It is widely recognized that the lack of adequate control mechanisms to regulate the voltages is a key hindrance. The goal of this project is to use AI and deep reinforcement learning to advance the current control designs by making them more data-driven and communication efficient. Depending on the candidate's qualifications and scientific interests, the project can be directed towards smart grid optimization, AI algorithm development or hardware implementations.

The research project entails the implementation and optimization of deep learning models for enhanced identification of biomolecules like peptides, proteins and metabolites from data acquired in hundreds of public and in-house liquid-chromatography mass spectrometry experiments. The candidate will use high-end deep learning methods to fully utilize the information contained in the output from these LC-MS experiments, with the aim to vastly reduce both instrument time and expenses. The application must include the following: • A letter of motivation, including details on qualifications within subject area (max. Should your referees wish to send their letters directly to us, please have them use e-mail: anc@sdu.dk And please note that these also need to reach us before deadline.

Postdoctoral fellowship in Applied Machine Learning and Information Retrieval is available at the Department of Computer Science, University of Copenhagen, Denmark. The application, in English, should be submitted electronically by clicking APPLY ONLINE above. The postdoctoral fellow will join the Machine Learning Section at DIKU. The Machine Learning section is among the leading research environments in Artificial Intelligence and Web & Information Retrieval in Europe (in the top 5 for 2020, according to csrankings.org), with a strong presence at top-tier conferences, continuous collaboration in international & national research networks, and solid synergies with big tech, small tech, and industry. The Machine Learning section consists of a vibrant selection of approximately 65 talented researchers (40 of whom are PhD and postdoctoral fellows) from around the world with a diverse set of backgrounds and a common incessant scientific curiosity and openness to innovation.

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.