The Systems and Data Analysis department at the Fraunhofer-Chalmers Centre for Industrial Mathematics intends to hire a student for an internship. You would be working under my supervision and contribute to the development of a platform for distributed data analytics by creating a web-based interface. The goal of this internship is to create a more intuitive alternative to the existing command-line interface.

Here is the job ad:

Internship (Web Development) at the Fraunhofer-Chalmers Centre for Industrial Mathematics

The Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
offers software, services and contract research for a broad range of
industrial applications. Modelling, simulation and optimization of products
and processes can boost technical development, improve efficiency and cut
costs of both large and small businesses. Since 2001, our highly skilled team
of mathematicians and engineers has successfully solved problems for more than
170 clients. We combine consultancy services with innovative research and
development based on a wide spectrum of competences.

We are looking for an ambitious student with a background in computer science
or related fields to assist in an ongoing applied research project in the
Systems and Data Analysis department.

Your task:
We have been developing a prototype for distributed data analytics. Currently,
the end user interacts with it via a Python library that is accessed through
a terminal window. In order to improve usability, we would like to add a HTML5-
based front-end to this system.

Required background:
- Experience with HTML5 and JavaScript
- Understanding of concurrency and asynchronous execution

Meriting:
- Experience with Python
- Awareness of Erlang

Your ideal profile:
- Chalmers student at the Master's level, preferably in the penultimate year
- Pursue a degree in Computer Science or a similar field
- Able to work independently
- Previous work experience in the software industry or as a student research
assistant

If you maintain a private code repository (Github, Gitlab, Bitbucket etc.),
then please highlight this in your application. If you have other samples of
work to show, such as a portfolio of projects on a blog or private website,
we would be keen to have a look.

This internship is a paid part-time (4h/week) fixed-term position until the
end of December 2018, with the possibility of extending the contract. The
starting date is flexible.

Contact persons:

Mats Jirstrand, Head of Department
mats.jirstrand@fcc.chalmers.se, 031-772 42 50

Emil Gustavsson, Applied Researcher/Data Scientist
emil.gustavsson@fcc.chalmers.se, 031-772 42 92

Gregor Ulm, Research and Development Engineer
gregor.ulm@fcc.chalmers.se, 031-772 42 71

Please send your application, marked "Contracted Student / SYS (OODIDA
Front-End)", consisting of a cover letter, CV, and a current academic
transcript covering your entire university education, to
recruit@fcc.chalmers.se.

Interviews will be held continually. Please apply as soon as possible.

www.fcc.chalmers.se

The Systems and Data Analysis department at the Fraunhofer-Chalmers Centre for Industrial Mathematics intends to hire a student for an internship. You would be working under my supervision and contribute to algorithm development in the realm of big data analytics. This internship is related to our work on Contraction Clustering (RASTER).

Here is the job ad:

Internship (Algorithm Development) at the Fraunhofer-Chalmers Centre for Industrial Mathematics

The Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
offers software, services and contract research for a broad range of
industrial applications. Modelling, simulation and optimization of products
and processes can boost technical development, improve efficiency and cut
costs of both large and small businesses. Since 2001, our highly skilled team
of mathematicians and engineers has successfully solved problems for more than
170 clients. We combine consultancy services with innovative research and
development based on a wide spectrum of competences.

We are looking for an ambitious student with a background in computer science
or related fields to assist in an ongoing applied research project in the
Systems and Data Analysis department. You will contribute to research in data
stream processing that is conducted in the area of distributed data analytics.

Your task:
- Implement a stream-processing algorithm, which was developed in-house
- Benchmarking
- Visualization
- Compare the implementation with other existing algorithms on a variety
of metrics

Required background:
- Functional programming in Scala or Haskell

Meriting:
- Experience implementing algorithms based on a mathematical specification
or pseudocode
- Algorithms/Machine Learning, in particular clustering
- Stream processing, in particular Apache Spark - Structured Streaming
- Data visualization, in particular Matplotlib

Your ideal profile:
- Chalmers student at the Master's level, preferably in the penultimate year
- Pursuing a degree in Computer Science or a related field
- Previous work experience in the software industry or as a student research
assistant
- Ability to work independently

If you maintain a private code repository (Github, Gitlab, Bitbucket etc.),
then please highlight this in your application. If you have other samples of
work to show, such as a portfolio of projects on a blog or private website,
we would be keen to have a look.

This internship is a paid part-time (4h/week) fixed-term position until the
end of December 2018, with the possibility of extending the contract. The
starting date is flexible.

Contact persons:

Mats Jirstrand, Head of Department
mats.jirstrand@fcc.chalmers.se, 031-772 42 50

Emil Gustavsson, Applied Researcher/Data Scientist
emil.gustavsson@fcc.chalmers.se, 031-772 42 92

Gregor Ulm, Research and Development Engineer
gregor.ulm@fcc.chalmers.se, 031-772 42 71

Please send your application, marked "Contracted Student / SYS (RASTER)",
consisting of a cover letter, CV, and a current academic transcript
covering your entire university education, to recruit@fcc.chalmers.se.

Interviews will be held continually. Please apply as soon as possible.

www.fcc.chalmers.se

The Systems and Data Analysis department at the Fraunhofer-Chalmers Centre for Industrial Mathematics intends to hire a student for an internship. The starting date is flexible, but the end date is firm, it’s the end of May 2018. You would be working under my supervision and contribute to algorithm development in the realm of big data analytics. This internship is related to our work on Contraction Clustering (RASTER).

Here is the job ad:


Internship at the Fraunhofer-Chalmers Centre for Industrial Mathematics

The Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC)
offers software, services and contract research for a broad range of
industrial applications. Modelling, simulation and optimization of products
and processes can boost technical development, improve efficiency and cut
costs of both large and small businesses. Since 2001, our highly skilled team
of mathematicians and engineers has successfully solved problems for more than
170 clients. We combine consultancy services with innovative research and
development based on a wide spectrum of competences.

We are looking for an ambitious student with a background in computer science
or related fields to assist in an ongoing applied research project in the
Systems and Data Analysis department. You will contribute to research in data
stream processing that is conducted in the area of distributed data analytics.

Your task:
- Implement a stream processing algorithm, which was developed in-house
- Compare the implementation with other existing algorithms on a variety
of metrics

Required background:
- Functional programming in Scala or Haskell

Meriting:
- Experience implementing algorithms based on a mathematical specification
or pseudocode
- Algorithms/Machine Learning, in particular clustering
- Stream processing, in particular Apache Spark - Structured Streaming
- Data visualization, in particular Matplotlib

Your ideal profile:
- Chalmers student at the Master's level, preferably in the penultimate year
- Pursuing a degree in Computer Science or a similar field
- Previous work experience in the software industry or as a student research
assistant
- Ability to work independently

If you maintain a private code repository (Github, Gitlab, Bitbucket etc.),
then please highlight this in your application. If you have other samples of
work to show, such as a portfolio of projects on a blog or private website,
we would be keen to have a look.

This internship is a paid part-time (4h/week) fixed-term position until the
end of May 2018. The starting date is flexible.

Contact persons:

Mats Jirstrand, Head of Department
mats.jirstrand@fcc.chalmers.se, 031-772 42 50

Emil Gustavsson, Applied Researcher/Data Scientist
emil.gustavsson@fcc.chalmers.se, 031-772 42 92

Gregor Ulm, Research and Development Engineer
gregor.ulm@fcc.chalmers.se, 031-772 42 71

Please send your application, marked "Contracted Student / SYS (OODIDA)",
consisting of a cover letter, CV, and a current academic transcript, to
recruit@fcc.chalmers.se.

Interviews will be held continually. Please apply as soon as possible.

www.fcc.chalmers.se


Our paper “Contraction Clustering (Raster): A Big Data Algorithm for Density-Based Clustering in Constant Memory and Linear Time” has been published in the Springer Lecture Notes in Computer Science series.

Here is the full citation with doi:

Ulm G., Gustavsson E., Jirstrand M. (2018) Contraction Clustering (Raster). In: Nicosia G., Pardalos P., Giuffrida G., Umeton R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science, vol 10710. Springer, Cham
https://doi.org/10.1007/978-3-319-72926-8_6

Alternatively, the submitted manuscript is available in my Gitlab repository:
https://gitlab.com/gregor_ulm/publications

At IFL 2017 in Bristol, UK, I presented our work-in-progress paper “Purely Functional Federated Learning in Erlang.” An update to this work will be presented as a poster at the upcoming 5th Swedish Workshop on Data Science (SweDS 2017), which will take place from 12 to 13 December 2017 in Gothenburg, Sweden. It is hosted by the University of Gothenburg. The abstract is reproduced below.

Functional Federated Learning in Erlang

Authors: G. Ulm, E. Gustavsson, and M. Jirstrand

A modern connected car produces gigabytes to terabytes of data per day. Collecting data generated by an entire fleet of cars, and processing it centrally on a server farm, is thus not feasible. The problem is that the total amount of data generated by cars, i.e. on edge devices, is too large to be efficiently transmitted to a central server. However, CPUs used in edge devices such as connected cars but also regular smartphones that connect to the cloud, have been getting more and more powerful in recent years. Tapping into this computational resource is one way of addressing the problem of processing big data that is generated by large numbers of edge devices.

One such approach consists of distributed data processing. Using the example of training an Artificial Neural Network, we introduce a framework for distributed data processing. A particular focus is on the implementation language Erlang. Arguably the biggest strength of the functional programming language Erlang is how straightforward it is to implement concurrent and distributed programs with it. Numerical computing, on the other hand, is not necessarily seen as one of its strengths.

The recent introduction of Federated Learning, a concept according to which edge devices are leveraged for decentralized machine learning tasks, while a central server only updates and distributes a global model, provides the motivation for exploring how well Erlang is suited to such a use case. We present a framework for Federated Learning in Erlang, written in a purely functional style. Erlang is used for coordinating data processing tasks but also for performing numerical computations. Initial results show that Erlang is well-suited for that kind of task.

We provide an overview of the general framework and also discuss an existing and fully realized in-house prototypical implementation that performs distributed machine learning tasks according to the Federated Learning paradigm. While we focus on Artificial Neural Networks, our Federated Learning framework is of a more general nature and could also be used with other machine learning algorithms.

The novelty of our work is that we present the first publicly available implementation of a Federated Learning framework; our work is also the first implementation of Federated Learning in a functional programming language, with the added benefit of being purely functional. In addition, we demonstrate that Erlang can not only be leveraged for message passing but that it also performs adequately for practical machine learning tasks.

Our presentation is based on our work-in-progress paper “Purely Functional Federated Learning in Erlang”, which we presented at IFL 2017. The context of this research is our ongoing involvement in the Vinnova-funded project "On-board/off-board distributed data analysis" (OODIDA), which is a joint-project between the Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers University of Technology, Volvo Car Corporation, Volvo Trucks, and Alkit Communications.