Graduation Internship: Reinforcement Learning for Logistic Optimization at Boskalis Canada

Papendrecht, South Holland, Netherlands -

Full Time

Start Date

Immediate

Expiry Date

05 Apr, 26

Salary

0.0

Posted On

05 Jan, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Version Controlling, AI Models, TensorFlow, PyTorch, Keras, MAS, RL, Mathematical Modeling, Scheduling, Data Science, Report Writing, Presentation Skills, Analytical Thinking, Initiative, Eager to Learn

Industry

Civil Engineering

Description

Company Description Working at Boskalis is about creating new horizons and sustainable solutions. In a world where population growth, increase of global trade, demand for (new) energy and climate change are driving forces, we challenge you to make your mark in finding innovative and relevant solutions for complex infrastructural and marine projects. Within a vibrant company culture you will be working as part of a diverse, international team of experts. We offer you the opportunity to realize your full personal potential and expand your professional career by creating new horizons. Together. Job Description Boskalis is currently looking for a student to start their graduation project with us. Logistic planning is a complex task involving numerous dependencies and a chain of events, especially in the large-scale projects that Boskalis conducts. Many projects share common challenges, such as aligning our assets, accounting for external dynamic factors, and meeting the objectives desired by venture partners. Traditional planning methods involve heuristic calculations guided by experience or rules of thumb, which are difficult to manage manually. Multi-Agent Systems (MAS) and Reinforcement Learning (RL) are promising fields worth exploring, as they have shown success in solving NP-hard problems through simulation and verification. The research project will require the student to conduct a literature review on developing such systems for our defined real-world projects. This will involve finding or building appropriate simulations to run these algorithms. Key design choices need to be made, such as defining the elements of the RL agent(s) and their environment. With a variety of RL algorithms and frameworks available, an educated choice must be made to address our specific problem effectively. In this research you will Work with other data scientists and meet with multi-disciplinary operational engineers to understand the challenge. Conduct literature research on MAS and RL and how they can be used for making schedules. Formulate the problem in a mathematical model. Develop efficient generic methods for making schedules for the chosen methods and demonstrate how they can be scaled up for future needs. Report your final findings, demonstrating your design choices and reproducibility of results. Your qualities You are doing a university master’s degree in computer science, AI, mathematics, or other related field. Affinity with Python and version controlling. Able to develop, train, and evaluate AI models, using TensorFlow, PyTorch, Keras. Theoretical understanding of MAS and RL, practical knowledge is not requested but pre. Proficiency in writing reports and findings, and presenting results. It is preferred to be working at our office in Papendrecht 3 days a week. Qualifications The resources that you will be able to use Receive supervision and support from data science experts from the Data Science & Engineering department. Gain access to necessary data and compute resources for training and deploying models (Databricks). Low threshold to plan meetings with people to gain a better understanding. Utilize well-established development facilities (Python, source code control, packaging). Access and review other developments that the Data Science & Engineering department are working on. What you can expect Internship guidance: We offer you the opportunity to get the most out of your internship by giving you the right guidance. Warm welcome: You can count on a warm welcome so that you quickly feel at home at Boskalis. Salary and more: As an intern, you will receive an internship allowance. In addition, we offer a fun work environment with lots of challenges. A dynamic work environment: An internship where you can learn a lot from a leading company and where you will be part of a diverse team of experts. Young Boskalis: Are you younger than 36? Then join Young Boskalis! Monthly social and sporting activities ranging from pub quizzes, yoga, bootcamps and an annual sailboat race. Networking and knowledge sharing are also an important part of Young Boskalis. Additional Information We are more than happy to answer your questions about the internship. Please contact Tim Gouweleeuw, Corporate Recruiter, via [email protected]. Interested? Please apply by filling in your details and by uploading your cover letter and CV on our internal careers site. Please note: Boskalis never requests a financial contribution to arrange visas or other documents related to a job vacancy. Be wary of individuals who pretend to be our recruiters and do ask for such contributions. Disclaimer for Recruiters and Recruitment Agencies We appreciate your interest in our vacancies and understand that your candidate might be enthusiastic about this exciting opportunity. However, our recruitment process is not structured this way, at Boskalis we handle recruitment ourselves. Therefore we do not accept unsolicited applications or CVs from recruitment agencies. Any submission will be treated as a direct application. #LI-TG1 Department: Corporate development Level of experience: Student What we promise 1: Plenty of room for personal development Organisation: Corporate Region: The Netherlands What we ask of you 3: Taking Initiative What we ask of you 1: Eager to learn Disciplines: Staff What we promise 2: Young Boskalis events and activities What we promise 3: Contributing to one of a kind projects What we ask of you 2: Analytical Thinking

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Responsibilities

The intern will conduct literature research on Multi-Agent Systems and Reinforcement Learning for logistic optimization, formulate mathematical models, and develop scheduling methods. They will also report findings and demonstrate design choices.