PhD student on Hybrid LCA-ABM of dairy farming systems including nonlinear optimization

Reference : ERIN-2019-045

Type: PhD Student
Contract type: Fixed term contract
Duration: 14 months (+ 22 months + potentially 12 months extension)
Place: Belvaux



The Luxembourg Institute of Science and Technology (LIST) is offering a 36 months (that can be extended up to 48 months) PhD position in the topic of Hybrid LCA-ABM of dairy farming systems including nonlinear optimization. The successful candidate will join the Environmental Sustainability Assessment and Circularity (SUSTAIN) Unit of the Environmental Research and Innovation (ERIN) department of LIST, which is developing knowledge, transferable technologies (e.g. software) and practical methods for the integrative evaluation and management of the sustainability of human driven systems. Within the SUSTAIN Unit, the applicant will be incorporated in the Life Cycle Sustainability Analysis (LCSA) Group. The activities conducted within the Life Cycle Sustainability Analysis group mainly consist in the development and application of methods, metrics and tools to assess the sustainability performance of products, technologies and policies for both industrial organisations and policy makers. The PhD student will be enrolled in the University of Luxembourg.

Dairy herd management activities have an important influence on environmental impacts of the agriculture and farming sector. The decision-making process concerning the different policies regulating those management activities could benefit from using tools that explicitly include behavioural aspects of farmers and sustainability information from the Life Cycle Assessment (LCA) point of view.  Agent-Based Models are especially suited to deal with behavioural components of human decision-making. The interconnection between ABM, LCA and optimization will substantially contribute to the objective of developing a suitable approach toward low emission farming.




The candidate will conduct a PhD thesis tentatively titled “Hybrid LCA-ABM of dairy farming systems including nonlinear optimization under environmental, technical and economic constraints”. The thesis is part of the project SIMBA (Simulating economic and environmental impacts of dairy cattle management using agent-based models) funded by the Luxembourgish National Research Fund (FNR) and the Fund for Scientific Research (FNRS) of the Wallonia-Brussels Federation.

The objective of the SIMBA project is to develop a decision support system (DSS) that incorporates behavioural models of dairy farmers using an ABM decision tool, which will be able to test different herd management strategies and to assess their environmental impacts. The DSS will build on top of an existing ABM simulator (called MUSASIM) for agriculture programmed in Java, developed in the framework of a past project.

The DSS will be tested in the case of dairy farms in Wallonia and Luxembourg.

In the framework of the SIMBA project, the selected PhD candidate will in particular be in charge of the enhancement of MUSASIM to take into account the herd management component and to integrate in it a farm optimisation model.

S(he) will interact with the University of Liège (Gembloux Agro-Bio Tech – GxABT), where another PhD student will work on the same project and will be in charge of the application of machine learning algorithms to predict phenotypes related to herd management and environment, based on the data collected on farm from the routine milk recording and economic data sheets.

The phenotypes predicted by GxABT will be integrated in the calculation platform developed by the candidate at LIST, which will allow an advanced calculation of the environmental impacts arising from the evolution of the dairy system (simulated by the ABM). The environmental impacts thus calculated will be used, together with the economic inputs influencing farmers’ decision-making, into the optimizer (that will be programmed by the PhD candidate) to find a set of pareto-optimal solutions.

Since a data analysis based on machine learning (ML) algorithms is foreseen in both parts of the work developed at GxABT and LIST, knowledge of ML applied to practical problems will be important and relevant for the work to be performed by the PhD candidate. A minimal knowledge of Economics principles will also be relevant for a correct implementation of the farms’ business model in the calculation platform. In these tasks the candidate will be assisted by the PhD advisor and other staff members at LIST, who have already experience on similar problems.

During the course of the PhD, periodical meetings and short stays at GxABT’s premises (Gembloux, Belgium) are foreseen, in order to exchange knowledge and updates about the advancement of the project and to promote cross-fertilization of the two complementary research teams.

The PhD candidate will be asked to present the results of the project at meetings and international conferences, write scientific articles and give short seminars.





  • Holds a MSc. Degree in Engineering, Computer Science or Applied Mathematics
  • As the research is very interdisciplinary, having a background in Mathematics, Physics, Statistics or other related fields is also considered a valuable asset
  • Previous knowledge and practical experience on Life Cycle Assessment (methodology, tools, and case studies) will be considered as a relevant advantage


  • Proven programming skills in at least one of the following languages: Java, Python, GAMS
  • Programming skills in R or Matlab will be considered as an asset
  • Proven organizational skills and interdisciplinary thinking, a problem-solving mind-set, and a strong team-working capability, but is also able to work independently and creatively


  • Flawless knowledge of English (both spoken and written) is required
  • Knowledge of German and/or French is considered as an asset


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Dr-Ing. Enrico BENETTO
Dr-Ing. Enrico BENETTO


 Stéphanie LUCADELLO

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