Rulex Explainable AI

Student opportunities

Are you a student or a research graduate?

We offer internship programs and thesis projects to help you put into practice what you have learnt in your university studies. Here at Rulex, you will learn and develop new skills, tutored by experienced professionals, who will ensure you make the most of your time with us.

Student opportunities

Student Opportunities

Kick start your career with us!

Team player

Team player

Become a team player by collaborating everyday with enthusiastic team members from all over the world.

Improve your skills

Improve your skills

Develop professional skills, practice time management, and improve your problem solving skills under the guidance of expert coaches.

Focus on your career

Focus on your career

Find out where your talents lie, and focus on your career aspirations, making the first step towards your next big goal.

Student Opportunities

Open Positions

Check out our list of open positions and get in touch. If you have a specific idea in mind, tell us about your project – we appreciate people thinking out-of-the-box.

Bio-inspired optimization and rule-based control on transport scheduling optimization (M. SC)

Bio-Inspired methods allow to optimize also complex systems when the optimum cannot be found with exact approaches. They are inspired by some natural process, such as Charles Darwin’s evolution theory. On the other side Rule-Based Control aims at optimizing complex systems by using a rule-based model obtained directly by the data. In this way, an explicit form of the cost function is not needed, increasing the range of potential applications. In this project the student will:

  • Study the state-of-the-art of Bio-Inspired methods
  • Definition of a case study in the field of public transport scheduling
  • Developing analysis workflows for optimizing the network
  • Analysing the results and optimizing the performances
Data driven rule-generation methods for the verification and validation of cyber-physical systems (M.SC.)

Verification and Validation (V&V) consists in ensuring that automated systems function in the intended way according to Safety, Cybersecurity and Privacy (SCP) requirements. The increasing integration and interconnection of modern Cyber-Physical Systems (CPS) incus an overhead on the V&V process making it time-consuming and costly. Machine learning can be applied to reduce the complexity of the V&V process highlighting more risky situations, that could better be investigated through standard techniques. During the thesis, the student will:

  • Study the state of the art of Verification and Validation techniques
  • Study the state of thew art of machine learning application in V&V
  • Develop novel approaches based on the combination between standard V&V methods and rule-based machine learning techniques
  • Test the new approaches on some datasets and evaluate the results in terms of precision and computational effort
Evaluating the stability of time-dependent set of rules (M.SC./B.SC.)

Rule-based decision methods are becoming crucial in modern process automation system since they allow expert users to understand the mechanism used by the machine to take decisions. Moreover, integration to manual rules (discovered by humans) and automatic rules (discovered by the machine) is a key point for improving automatic decision processes. To this aim, it is essential to transform the space of the rules into a metric space, in order to allow operations such as distance computation.
The student will:

  • Study a way to evaluate the distance between sets of rules
  • Implement these definitions
  • Design a case study to evaluate the effectiveness of this approach
  • Perform the tests and evaluate the results
Improving the performances of rule-based machine learning methods through a sequential ensemble approach (M.SC./B.SC.)

Explainability of machine learning algorithms is one of the most important challenges in today Artificial Intelligence. As a matter of fact, understandable models can be audited by expert human users, allowing a quicker and sounder implementation in automated decision systems. Rulex has developed an innovative rule-based machine learning algorithm, named Logic Learning Machine. The objective of the thesis is to develop an algorithm independent sequential ensemble approach and to apply it to Logic Learning Machine to improve the prediction ability. These are the main activities:

  • Using the Rulex platform, developing a workflow for the generation of ensembles of classifiers and sequentially applying them to a dataset
  • Applying this workflow to Logic Learning Machine
  • Test the approach to some benchmark dataset and compare with results obtained with standard methods
Missing imputation effect on classification performance (B.SC.)

Often, when statistical and machine learning techniques are applied to real-world data, the data analyst needs to face the fact that at least some of the features include missing values. Excluding samples including missing features from the analysis can significantly decrease, even up to the point to make this choice unfeasible. To this end, missing imputation techniques such as MICE (Multiple Imputation by Chained Equation) have been developed. Of course, the adoption and tuning of one of such techniques is expected to have an impact on the performance of the following modelling phase. The student will:

  • Study a way to evaluate different techniques of missing imputation
  • Implement a subset of these techniques
  • Design a case study to evaluate the impact of (different kinds of) missing imputation on the performance of different classification algorithms
  • Perform the tests and evaluate the results
Rule-based methods for unsupervised detection of anomalies (M.SC.)

Detecting anomalies in a set of data is a crucial task almost all application field. Sometimes anomalies are manually flagged by human users that, thanks to their experience, are able to distinguish regular cases from anomalous ones. Nevertheless, in most cases anomalies are hidden in the data and should be discovered without any human supervision. For this reason, unsupervised methods for anomaly detection have been extensively studied in the last years. One possible choice is given by auto-encoding methods that compare the prediction provided by supervised methods with the actual one to determine the level of anomaly of a pattern. Different supervised methods can be used in this context. In this thesis the student will explore the possibility of using a rule-based classification method. In particular, the student will:

  • Study a way to use autoencoding rule-based methods to detect anomalies
  • Implement these definitions
  • Design a case study to evaluate the effectiveness of this approach
  • Perform the tests and evaluate the results
Rule-based robotic data correction (M.SC.)

The problem of incorrect data is a big challenge for most companies since it affects all the data-based decision processes resulting in higher costs. Robotic Data Correction (RDC) is an innovative tool, developed by Rulex, to automatically correct data. RDC is a general-purpose tool that corrects also interdependent errors in data. The activities to be carried in the thesis are related to one or more of these objectives:

  • Exploring new applications of RDC
  • Extending the application of RDC to ordered variables
  • Developing analysis workflow ready to be deployed in real world scenarios

The candidate will study how RDC works, develop some improvements to the current methods and test their effectiveness on real world and/or synthetic data.

student opportunities

Testimonials

Hear what some of our staff have to say about how they started their careers in Rulex

Working and writing a thesis at the same time is not easy, but Rulex supported me every step of the way. I worked as a technical communication intern, and it was a unique way for me to learn new skills. What I find special about this company is that it allows young people to gain on-the-ground experience and take on new challenges under the guidance of motivated professionals. For me, working in Rulex is a constant learning journey.

Silvia Parma

I graduated in Mathematical statistics and data processing with a thesis project in collaboration with Rulex. Here, I found a welcoming, young, and stimulating working environment, where I could grow professionally in my career, putting into practice my expertise and freely expressing my ideas and personality.

Alessandro Piazza

I started my curricular internship with Rulex after hearing a coursemate talking about the company enthusiastically. After only one week at Rulex, I was overwhelmed by the team’s passion. What struck me most was the collective desire to grow together and the internationality of the company. During my internship, I had the chance to put into practice what I had studied, but also to dive into new topics, such as the world of machine learning.

Enrico Allia

student opportunities

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