Project Assistant for coupling machine learning and vegetation modelling - Hiring in process/Finished, not possible to apply

Work duties
The staff member will be expected to lead/contribute to research coupling machine learning algorithms into the LPJ-GUESS dynamic global vegetation model. Tasks will include the planning of model developments, training of appropriate algorithms and their coupling into LPJ-GUESS, designing experimental protocols, carrying out the related simulations and analysis, and leading writing of peer-reviewed papers based on the results. Exact topics and work plans will be agreed in discussion with their supervisor, but there is likely to be a focus on drought impacts and the staff member is expected to contribute substantially to the intellectual development of the work. The staff member will also be expected to contribute to or lead applications for external funding as necessary. Participation in national and international meetings and workshops related to the above is expected, as is carrying out related administration and communication activities.

Qualifications

Requirements

• Education: Bachelor and Master's degrees including strong proficiency in mathematics and physics
• Very good oral and written proficiency in English
• Substantial prior experience developing and applying the LPJ-GUESS dynamic global vegetation model
• Experience in the application of machine learning techniques to environmental problems
• Documented skills in computer programming, including lower-level languages such as C++
• Documented success in leading and contributing to high-quality scientific publications

Other merits

• Specialist knowledge in forest dynamics or tree ecophysiology, particularly relating to drought response
• Evidence of ability to develop and execute own research ideas, and act upon own initiative
• Experience in working with high performance computing clusters
• Strong organisational skills, including ability and willingness to contribute to community activities
• Emphasis will be placed on academic achievements as evidence of emerging independence as a researcher and the ability to communicate in scientific and outreach contexts

We will place great emphasis on personal suitability. Consideration will also be given to the potential to develop an independent research career in the future, collaboration skills, how the applicant’s experience and skills complement and strengthen the administrative work within the unit/department, as well as contribute to its future development.

Terms of employment
Fixed-term employment at 100% of full time for a period of 1 year. Preferred starting date, 1st November 2021.

For further details contact thomas.pugh@nateko.lu.se

Instructions on how to apply

Applications shall be written in English and be compiled into a PDF-file containing:

• an application letter detailing your motivation for this position, a general description of past research work and future research interests (no more than one page)
• résumé/CV, including a list of publications
• contact information of at least two references
• copy of certificates/grades that you wish to be considered