148 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Technical University of Denmark
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here
-
within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
-
, or a related field. Have documented experience in some of the following: Computational materials modelling or quantum mechanical simulations (e.g. DFT, MD). Machine learning / deep learning (preferably
-
about DTU Electro at https://electro.dtu.dk/ . If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Furthermore, you have the
-
academia and with clinical or industrial partners. Teach and supervise at the BSc, MSc, and PhD levels. Contribute to DTU Bioengineering’s research environment, including shared infrastructures
-
for teaching and research CV including employment history, list of publications indicating scientific highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation
-
an analytical laboratory (e.g. performing enzyme assays, chromatography, water quality analyses) An interest in learning new techniques Good analytical skills and communication abilities in English As a formal
-
of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling
-
with local colleagues and be embedded into large international collaborations. You are expected to teach and take course appropriate for the graduate level at DTU. You must have a two-year master's
-
-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory