Issue Date: 7 February 2022
Ocean Mineral Singapore Pte. Ltd. (OMS) pursuant to its contract for the exploration for polymetallic nodules signed with ISA on 22 January 2015 is offering one (1) three-month virtual internship to candidates from developing States under its 2022 training programme.
The internship will be provided by the National University of Singapore (NUS) between May and August 2022 (Exact start date TBD)
The Clarion-Clipperton zone (CCZ) is a large area spanning several million square kilometers. In order to plan exploration and exploitation efficiently in such a large area, a guiding model of the spatial variation of polymetallic nodules is needed. This model should be able to predict variations of nodule resource parameters by utilizing our understanding of the nodule formation process and the limited available distribution data from explorations.
Some challenges exist to achieve exact quantifications of nodule parameters based on their biogeochemical formation mechanisms due to the vast complexity of processes leading to nodule formation. A model is therefore required that can bridge the observed variations in the acquired data to the known mechanisms via a data-driven approach. From this perspective, artificial neural networks (ANNs) are ideal tools for modelling of PMNs as they are effective in characterizing unknown underlying variations encountered in data.
The selected intern will work with NUS Acoustic Research Laboratory (ARL) to improve upon existing Machine Learning (ML) based modelling of the spatial distribution of nodule parameters by tapping into more powerful ANN architectures and modelling techniques. These include, but are not limited to, using the power of probabilistic ML to quantify uncertainty of model predictions, and trying to model more element percentages with a view towards commercial feasibility studies. These approaches will aim to use the limited dataset available as efficiently as possible to draw inferences about the modelled region of interest.
Candidates must at minimum, meet the following requirements:
- Education: A good Master’s degree in a Computer Science or Electrical Engineering, or at least a Second Class Upper Honors degree or equivalent in a similar discipline.
- A strong interest in doing research in the topic described above.
- Prior knowledge required: Machine Learning, Neural networks. Knowledge in probabilistic Machine Learning (e.g. Tensorflow probability) would be valued.
- Tools: Python, TensorFlow.
- Language: Proficiency in both written and spoken English.
The trainee will be required to sign a confidentiality agreement with OMS.
The trainee, together with an appointed supervisor, will work to complete the internship to ensure the programme structure aligns itself as best as possible towards the training and capacity developmental needs of the participant's country of origin.
Upon completion of the training, the trainee, assisted by the supervisors, is expected to submit a report on the outcomes of their training to OMS and then to the Authority.
Applications should be submitted not later than 4 April 2022 either through:
(i) The online training portal. Users applying through the online portal, must upload a copy of a passport photo, the signed nomination, degree / certificates and résumé; or
(ii) Email, in one of the two working languages (English or French) of ISA Secretariat, to firstname.lastname@example.org using the correct forms below.
- Application form (docx| pdf) to be completed by the applicant;
- Nomination form (docx| pdf) to be completed by the institution or governmental department;
- Copy of Degree and other relevant Certificates;
- Proof of English;
- Copy of CV or Résumé.
One of the Voluntary Commitments that the ISA made at the UN Ocean Conference 2017 was ‘Enhancing the role of Women in Deep-Sea Research through Capacity Building’ – we therefore strongly encourage suitably qualified female to apply for these training opportunities.