Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus University Denmark

Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus University Denmark

Conditions

• Shortlisting is used; the head of department selects candidates for evaluation.
• Referees must be contacted before the application deadline to ensure letters are submitted on time.
• Letters of reference received after the application deadline will not be considered.
• Salary depends on seniority as agreed between the Danish Ministry of Taxation and the Confederation of Professional Associations.
• Formalities and salary range
• The university offers an inspiring research and teaching environment to its 38,000 students (FTEs) and 8,300 employees, and has an annual revenues of EUR 935 million.

Tasks

• Develop hybrid models integrating limnological knowledge into machine learning models using the Knowledge-Guided Machine Learning (KGML) paradigm.
• Explore novel ways to infer information from environmental data to update scientific conceptual models.
• Develop the methodology of modular compositional learning (MCL) by decomposing aquatic ecosystem models into modular sub-components.
• Apply MCL methodology to critical ecological processes such as vertical turbulent diffusion, phytoplankton production and consumption, and greenhouse gas emissions.
• Compare performance against several 1D aquatic ecosystem models to evaluate hybrid MCL model improvements.

Requirements

• A PhD in environmental engineering, ecology, environmental science, biology, data science, computer science, or a closely related field.
• Ability to work closely with other researchers, groups within the Department, and external end users.
• Ability to collaborate and build professional relationships.
• Submit an application in English including a CV, degree certificate, publication list, research plans, and teaching portfolio.
• Qualifications must meet the Memorandum on Job Structure for Academic Staff at Danish Universities.
• The Department currently employs approximately 300 academic and technical staff, as well as many PhD students.
• The qualified candidate will work in a supportive and internationally engaged scientific environment at the Section for Freshwater Ecology.

Preferred

• Knowledge of freshwater ecology and/or physical limnology.
• Experience with numerical modelling.
• Experience with programming languages, especially Python, and familiarity with NumPy, SciPy, and Pandas.
• Experience with scientific programming.
• Experience with scientific writing.
• Excellent written and spoken English.
• Interest in deep learning and Knowledge-Guided Machine Learning.
• Interest in scikit-learn, PyTorch, and TensorFlow.

Benefits

• Access to weekly water quality sampling and turbulence profiles for training and testing.
• Use of the MCL methodology to replace uncertain process descriptions with deep learning models.
• Training and growth opportunities in AI for candidates with minimal experience.
• Access to a well-developed research infrastructure, laboratories, and shared equipment.
• Opportunities for interdisciplinary collaboration across Ecosciences focus groups.
• Mentorship and support in developing independent research directions.
• Support for grant writing and professional development.
• Relocation services and career counselling for expat partners.
• Access to the Junior Researcher Development Programme for postdocs.