Student project: Nutrient
estimation in algae bioreactors using AI
What you will do
Hydroponic horticulture is expanding all over Europe, leading to a waste stream of thousands of m3 of drain water with high nutrient content, which need to be recycled or be disposed of, potentially causing environmental stress. The REALM project identified an opportunity for this drain water to be used as nutrient solution in the cultivation media of microalgae, at reduced cost, while bringing sustainability to both microalgae and hydroponic sectors. Within this project, OnePlanet is developing an automated inline spectroscopic sensor system to monitor the two main nutrients of both hydroponic and microalgae cultivation: nitrate (NO3-) and phosphate (PO43-). Below a list of relevant research topics that are being addressed in this project and that can be part of the content of this internship:
- Develop an analysis model that incorporates the non-linear features of the spectral data to improve the accuracy of the predicted nitrate concentrations.
- Develop a hybrid AI/analytical approach, that combines data-driven machine learning with our knowledge of the physics of nitrate absorption, the biology of
- microalgae and the engineering of the sensor with into a single analysis model with improved performance.
Enhance precision through the interpolation of ground truth data, ensuring a comprehensive and accurate representation of the nitrate concentration.
- Develop analysis models that not only predict concentration values of nitrate and phosphate, but also provide a reliability or accuracy score thereof
- Develop analysis models to predict or detect other important parameters in the algae cultivation, like pigments, biomass, algae species, biocontamination, or stress.
- Implement an intelligent selection process for samples destined for lab analysis, by strategically identifying and scrutinizing 'unexpected' samples.
- Implement advanced predictive maintenance techniques for automated issue detection, leveraging the insights from reference samples to ensure proactive and efficient management.
The internship work and activities will be organized in a Scrum methodology. Prioritized tasks will be selected from the backlog and will be tackled and evaluated on a biweekly basis. At the end of each biweekly iteration, you will showcase the progress made and will reflect on gained insights and possible improvements to focus on. Additional stakeholders or users may make part of the showcases to get better feedback on the developed product. An initial backlog for the internship will be built based on the projected uses mentioned above.
- Data pre-processing and feature extraction based on domain knowledge, literature and exploratory data analysis.
- Creating data visualizations to share with stakeholders (internal and external) and end users.
- Developing methods for predictive maintenance, intelligent selection of samples for lab analysis, interpolation of ground truth.
- Use machine learning and time series analysis techniques for pattern detection and classification.
- Explore ways to use the developed ML (or relevant) techniques to support decisions.
- Collaborate and brainstorm with other team members and experts, provide regular update on status and results.
What we do for you
- We have a challenging problem where you have freedom to explore and deliver solutions that have positive environmental and societal impact.
- We have a diverse team of experts in data science, machine learning, hardware, software, sensors, biology, and agriculture who can coach you and provide you with advice in the development of the assignment.
- You will join the Digital Twin team of OnePlanet, which employs state of the art knowledge on machine learning for precision food production and the frameworks necessary to perform these big data tasks at large scale.
- You will be able to exchange views and knowledge with the OnePlanet and Imec community of experts and scientists, widening your professional network.
- We can help you improve your coding skills up to industry standards.
- You will have access to our cloud infrastructure to solve this problem allowing you to process large amounts of data within reasonable times.
Who you are
- Knowledge of Python
- (Advanced) Knowledge of supervised and unsupervised machine learning algorithms, testing and validation methods.
- Knowledge of time series analysis and anomaly detection.
- (Advanced) Experience with Numpy, Pandas, Matplotlib, Scikit-Learn.
- Experience with PyTorch or TensorFlow is a plus.
- Knowledge of Git.
- Basic understanding of Agile-Scrum.
- You are passionate about bringing positive impact to environmental and societal challenges.
Does this position sound like an interesting next step in your career at imec? Don’t hesitate to submit your application by clicking on ‘APPLY NOW’.
Got some questions about the recruitment process? Martijn Kohl of the Talent Acquisition Team will be happy to assist you.