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Industrial PhD for Network Design – Transported by Maersk Data & Analytics

Maersk Group - A.P. Møller Mærsk



Are you ready to revolutionize global logistics and make a real-world impact with your research? Apply for Maersk's Industrial PhD Fellowship and be at the cutting edge of Operations Research and Machine Learning. Collaborate with top experts, develop state-of-the-art techniques, and drive innovation in liner shipping network design. Your journey to transforming the future of global trade starts here!

Maersk is improving life for all by integrating the world. From the farm to your refrigerator, or the factory to your wardrobe, we are developing software solutions to support our customer needs from one end of the supply chain to the other. Technology is taking center stage in enabling our customers to trade globally and we are building our core products in-house. Join us to help re-think what technology can do and develop cutting edge solutions to support our global logistics business.

Your new job

You will join the Transported by Maersk (TbM) Data & Analytics team as an Industrial PhD Fellow, in collaboration with the Technical University of Denmark, DTU, who provide a scientific supervisor. You will be developing and implementing solution methods based on state-of-the-art Operations Research and Machine Learning techniques to find effective large-scale liner shipping networks that balance multiple objectives, such as cost, schedule robustness and sustainability. In this context, you will explore the potential of ML methods, such as Graph Neural Networks, at predicting promising parts of the network. This will lead to a reduction in problem sizes, enabling the applicability of existing OR solution methods in practice.

You are also expected to contribute to the liner shipping research community by creating and making publicly available benchmark instances that are larger and more realistic than those currently available.

You will collaborate closely with other data and decision scientists in TbM Data & Analytics, as well as with business stakeholders to ensure that the solution methods you implement support the evolution of network design capabilities at Maersk.

Your new team

We are a cross-functional team of data scientists, decision scientists, data engineers and software engineers working together to build reliable, performant and maintainable analytics software.

We are accountable and proud of our work. Our culture is informal and built on respectful collaboration, experimentation, and continuous improvement. We spend time together at team events and hackathons to form a stronger, more collaborative unit and to inspire and grow each other.

Key Responsibilities

You are expected to:

· Develop novel methodologies to solve the liner shipping network design problem, leveraging the domains of Operations Research and Machine Learning to uncover mechanisms to reduce the complexity of the problem space.

· Implement solution methods as software components that can be accessed from existing Network Design software solutions

· Publish results in international journals and present results at international conferences.

· Attend relevant courses at DTU or other universities (30 ECTS in total).

· Spend 4 months abroad with a research group at a university outside Denmark.

· Collaborate with colleagues in the TbM Data & Analytics organization.

· Share learnings and discoveries through regular knowledge sharing events.

We are looking for

· You have a background in applied mathematics, statistics, operations research, machine learning or in a similar technical field at MSc level.

· You have excellent grades and have demonstrated experience of research in a relevant field, ideally with conference and/or journal publications.

· You have strong programming skills.

· You can drive a long-term project independently.

· You are motivated by working on hard problems and ensuring the models you work on deliver high quality results and provide maximum value for our customers.

· You are collaborative by nature, care about others and strive to both share your knowledge and learn from others.

· You are a strong communicator, capable of disseminating your knowledge and key findings to both technical and non-technical audiences in an easily understood manner.

· You care about building great software products that can be used by others, so that your research can be directly applied to the products we deliver in TbM Data & Analytics.

Please include a motivation letter in addition to your CV when applying.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing [email protected].

Work type:
Full time

Locations:
DK - Copenhagen

This job posting is collected from company pages and is only shown as short resume. Read entire job ad here:

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Maersk Group - A.P. Møller Mærsk

Main office: Esplanaden 50, 1098 København K

A.P. Moller - Maersk is an integrated container logistics company. Connecting and simplifying trade to help our customers grow and thrive. With a dedicated team of over 76,000, operating in 130 countries; we go all the way to enable global trade for a growing world.

We offer graduate programmes: Read more here
We perodically offer student internships

More info for this company

Graduate programme Talent demand All current jobs


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