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materials science, and machine-learning-driven materials discovery. The successful candidate is expected to have experience in areas such as high-throughput DFT calculations, thermodynamic and phase
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analytically underexplored conditions. It relies on modern machine learning approaches to model fragmentation behavior, support spectral interpretation, and enable future advances in peptide identification
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, and open scientific resources where relevant Required Qualifications Applicants must have: A PhD in computational proteomics, bioinformatics, machine learning, computer science, chemometrics
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learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of
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Employment - Hours Full time Duration Fixed term Qualification PhD Sector Academia Apply on website (This will open in a new window from which you will be automatically redirected to an external site
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particular pollen and fungal spores. The geographical focus is on both the urban environment and agricultural areas in Denmark • You will be contributing to the dynamical integration of near real
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which you will be automatically redirected to an external site after 5 seconds) Save job Click to add the job to your shortlist Send job Job Details Are you interested in using machine learning to
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overall objective of developing robust and deployable physics-driven machine learning solutions for TEM data. In addition to the colleagues at Department of Electrical and Computer Engineering, you
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of up to 2 years. The place of employment will be in Odense. Research profiles and tasks We are looking for a candidate with a background in computational social sciences, machine learning or data
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agricultural areas in Denmark You will be contributing to the dynamical integration of near real-time data into WRF-Chem You will be contributing to the development of machine learning models used on data