苏黎世联邦理工

Rearing of Fly Larvae with Machine Learning to Improve the Sustainability of Agricultural Inputs

项目介绍

ETH Zurich is one of the leading universities of the world with a strong focus on science and engineering. In 2010 it established the Singapore-ETH Centre (SEC) in collaboration with the National Research Foundation (NRF) to do interdisciplinary research on pressing problems.

The centre currently runs several research programmes with the Future Cities Laboratory (FCL) as one of the programmes. It is home to a community of over 100 PhD, postdoctoral and professorial researchers working on diverse themes related to sustainable cities and resilient infrastructure systems. In the course of their work, researchers actively collaborate with universities, research institutes, industry, and government agencies with the aim of offering practical solutions.

The ETH Sustainable Food Processing Group in the Department of Health Science and Technology uses a combined approach of emerging food safety concepts, novel protein processing and sustainability assessment to target fundamental challenges in food science and society. Our research lies at the interface of biological systems and engineering.

Project background

Singapore targets increasing its domestic, independent food supply under urbanized constraints by 30% through intensification of domestic food production by 2030. The NRF funded project “Using black soldier flies in food waste management and sustainable food production in urban systems” supports the ‘30-by-30’ vision by insect-based bioconversion of high value food waste back for production of high-quality agricultural inputs for domestic food production. The project includes collaborative research by National University of Singapore (NUS), SEC, ETH Zurich, Nanyang Technological University (NTU) and others, across five interrelated thematic areas.

The doctoral student will work with larvae of the black soldier fly (Hermetia illucens). Rearing of black soldier fly larvae on food wastes is notoriously unreliable due to the natural variable nutrient composition among wastes. In large rearing facilities, such variability can be partially compensated by formulating waste mixtures. However, the large space requirements in these facilities limits direct replication in dense urban systems like Singapore. Instead, decentralized and vertical rearing in unutilized urban spaces holds more promise for Singapore. The doctoral student will optimize insect bioconversion in a decentralized rearing module by using the ever-evolving pool of smart and inexpensive sensors and machine learning algorithms.

Job description

As a Doctoral Candidate in the ETH Sustainable Food Processing Group in Singaporeyou will be responsible for research working towards making decentralized food waste bioconversion by black soldier fly larvae more reliable.

Your tasks will include:

  • Design and commission a modular research rearing unit using the ever-evolving pool of smart sensors, e.g. real time analysis of growing crate temperature heat maps, larval movement (e.g. using bioacoustics) and emissions/odours (e.g. H2O, CO2, NH4, CH4)
  • Operate the rearing unit to produce a training data set for construction of a machine learning algorithm that adapts rearing parameters to make food waste decomposition more reliable
  • Validate rearing unit and machine learning algorithm with both industrial and community partners to ensure uptake and replication into Singapores food system
  • Communicate research outcomes at relevant international conferences and in peer-reviewed journals
  • Acting at the interface between science and technology when supervising students and collaborating with partners from academia and industry

Your profile

  • A M.Sc. (or equivalent) in Bioinformatics, Computational Biology, Environmental or Mechanical Engineering, Environmental Data Science and Machine Learning, Measurement and Control Technology (or similar)
  • A passion for sustainability, circular economy, and resource management
  • Expertise and interest in working with biological systems (e.g. animals, insects, microbes, vertical farming of plants)
  • Interest to work with fly larvae and food waste
  • Experience in applying Machine Learning, Data Analysis and Statistics (e.g. Regressions) in Python
  • Good “people skills” when interacting with colleagues, supervisors, research partners and research equipment manufacturers
  • A strong interest and willingness to prototype, build, operate and trouble-shoot a sensor-based research setup
  • Experience of working independently in an international multicultural project
  • Strong verbal and written communication skills in English
  • Scientific curiosity and motivation to perform scientifically rigorous experimental work

The following competences will be advantageous:

  • At least one peer-reviewed publication

Interested?

Applicants submitting the following documents will be considered for the position:

  • Cover letter outlining your motivation and experience in the field;
  • One pager conceptually describing your first ideas of a modular sensor-based fly larvae rearing unit (with text or visualized);
  • CV including two referees (name, affiliation, position, email address), associated certificates (e.g. Master’s and Bachelor’s degree) and transcript of records.

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

For further information about our research and projects, please visit the ETH Sustainable Food Processing (SFP) Group website, summarizing our ongoing insect-based bioconversion research. Questions (not applications) regarding the position should be directed to Dr. Moritz Gold; moritz.gold@hest.ethz.ch (no applications).

录取要求

  • A M.Sc. (or equivalent) in Bioinformatics, Computational Biology, Environmental or Mechanical Engineering, Environmental Data Science and Machine Learning, Measurement and Control Technology (or similar)
  • A passion for sustainability, circular economy, and resource management
  • Expertise and interest in working with biological systems (e.g. animals, insects, microbes, vertical farming of plants)
  • Interest to work with fly larvae and food waste
  • Experience in applying Machine Learning, Data Analysis and Statistics (e.g. Regressions) in Python
  • Good “people skills” when interacting with colleagues, supervisors, research partners and research equipment manufacturers
  • A strong interest and willingness to prototype, build, operate and trouble-shoot a sensor-based research setup
  • Experience of working independently in an international multicultural project
  • Strong verbal and written communication skills in English
  • Scientific curiosity and motivation to perform scientifically rigorous experimental work

申请亮点

  • At least one peer-reviewed publication

项目概览

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截止日期 2021-10-14
苏黎世联邦理工

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苏黎世联邦理工是国际研究型大学联盟、全球大学高研院联盟、IDEA联盟成员,是闻名全球的世界顶尖研究型大学,连续多年位居欧洲大陆高校翘首。
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