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.

AMQP broker optimization in distributed system (B.SC.)

A distributed system in cloud applications is a set of different containers which runs different codes in a correlated way, with a high level of internal communication between the parts. We used Microservices are referred to as containers exploiting a particular operation inside our system. The main used system of communication in microservices distributed system is the AMQP protocol, a broker mediated protocol that allows a high level of control about message retaining, queueing and exchange. The main AMQP broker used in many commercial and open-source application is RabbitMQ. Scope of the thesis is to study this type of communication in a complex microservice structure where many types of different queues and exchange are present and where a high level of security and performance is strictly required. After a benchmark testing phase the broker configuration will be optimized to speed up the whole system, using the most advanced data analysis technique available on the market.

How to Apply

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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
How to Apply

Click HERE to fill in the application form below

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
How to Apply

Click HERE to fill in the application form below

Decision Support System for semi-autonomous vehicle based on Explainable AI

Rulex is joining a European consortium to develop a Driver Monitoring System (DMS), which can classify both the driver’s cognitive states (distraction, fatigue, workload, drowsiness) and the driver’s emotional (anxiety, panic, anger) state and intention (turn left or right), as well as the activities and position of occupants (including driver) inside the vehicle cockpit. This information will be used by a Decision Support System (DSS) for autonomous driving functions, including take-over-request and driver support. The student will:

  • Analyze the state of the art in the field
  • Gather information about the driver coming from different sensors in a simulated environment
  • Apply Explainable AI algorithms to the data
  • Analyze the results and integrate them in the system architecture
How to Apply

Click HERE to fill in the application form below

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
How to Apply

Click HERE to fill in the application form below

Explainable AI for image recognition

Many of the cutting-edge applications in Artificial Intelligence involve the analysis of images and video streams. Even if the accuracy of methods (such as Convolutional Neural Networks – CNN) for recognizing objects and person in images has become higher and higher, still some systematic errors may happen, in particular when the classifier is trained on a not sufficiently representative set of images. Many of these errors are originated by the impossibility of understanding why a certain classification has been done. To solve this problem, recently Explainable AI has been proposed to help people to understand the functioning of AI models. In this thesis the student will:

  • analyze state-of-the-art techniques for Explainable AI applied to images
  • implement some of them
  • develop new techniques to face specific gaps and
  • compare the results of the analyzed methods on standard datasets
How to Apply

Click HERE to fill in the application form below

Grid Representation for general advanced type (B.SC.)

In many Data Analysis applications not homogeneous data need to be treated. Data spans from number to images, from text to geographical coordinates and for each of these types of particular operations need to be implemented and offered to Data Scientists. One of the most difficult challenges in Data Analysis is data graphical representation: data are mostly visualised through a table grid, sometimes ill represented in a grid cell. The scope of this thesis is to implement a general approach to store any form of derived data (images, geographical coordinates, credit card number and so on) in a column table maintaining a fast and clear visualization. Users should be able to perform specific operations owned by the derived type (length between two geographical quantity, opacity filter for images and so on).

How to Apply

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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
How to Apply

Click HERE to fill in the application form below

Machine Learning for Load balancing in real-time computation (M.SC.)

The most important feature of a distributed cloud application is the balance between its internal components. CPU and RAM are provided in a distributed form and the ability of the software itself to control the load between the various parts is the key feature of this type of architecture. To reach this goal, an estimate (given the operation) of the necessary CPU and RAM to complete it represents a primary input. However, in many complex scenarios a reliable estimate of such quantity is far from being trivial. The scope of this thesis project is to try to apply Machine Learning technique to this field to construct a Load Balancer Application. The Load Balancer Application will be able to learn for the precedent cases and apply autoregression techniques to better estimate the effort of any operation on the architecture itself.

How to Apply

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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
How to Apply

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NoSQL DB implementation for big data analysis (M.SC.)

Databases are the most used type of storage in data analysis, in cloud application and in software in general. They represent the most used storage class in almost all the possible fields. Databases are divided in SQL and NOSQL macro types. SQL Databases are more optimized in treating structure data but they depend on SQL syntax and they are far from being general: each SQL database distribution has its SQL syntax, its behaviour and its performance. Moreover, conflicts management (read/read, read/write, and write/write parallel access) is not treated correctly in many used distributions, leading to unexpected results in case of high parallelism as in any cloud application. For this reason, in recent years NOSQL database has grown up to fill this lack of old SQL database. NOSQL database relies on different file system logic. They allow not structured data to be stored more efficiently; they correctly treat conflict management and allow to perform simple query with high performance due to index replication. On the contrary however, they are extremely slow in fetching high quantity of data with respect to a standard SQL database, preventing them to be used in big data analysis cases. The scope of this thesis project is to implement a NOSQL database with some innovative ideas to speed up the fetching of data reducing as much as possible computation time and memory consumption (at least for simple single value queries). A comparison with standard SQL and NOSQL distribution will also be performed.

How to Apply

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Piece-wise linear regression algorithm (M.SC.)

In machine learning, single and multivariate linear regression represent two of the most used algorithms to approximate a set of data with a fitting function. The searched function is a linear function in the single case, or a polynomial function in the latter. However, both the solutions return a continuous function which in some cases could be inaccurate, especially to treat fast changing process. The scope of this thesis project is to implement an innovative approach in which regression is applied searching for a piecewise linear function, with a fixed number of breakpoints. Later the behaviour of the piecewise approximation will be compared with single and multivariate solution and with other ML regression system as LLM, SVM and Neural Network.

How to Apply

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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
How to Apply

Click HERE to fill in the application form below

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.

How to Apply

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Scaling methodologies on stateful distributed system (B.SC.)

In cloud applications, any software should in principle be able to manage many concurrent accesses, permitting different users to connect to the same components without a real knowledge about who is using together the cloud platform. To reach this situation and avoid high maintenance costs, the constructed solution should be scalable and balanced. Recently, the state of the art about cloud architecture is based on the micro-services structure. In this framework, the whole system is divided in tiny components, each one deployed and scaled separately. Each tiny component is responsible for a unique operation or for a set of operations. However, in all the existing applications of this structure the main requirement of the set of operations performed by a single micro-service is to be stateless, meaning independent from any information contained especially in the RAM of the micro-service itself. This feature limits the structure to be applied in complex cloud applications where a concept of user session has to be included.
This thesis project aims to study possible extensions on this subject and implement possible solutions to construct a stateful scalable micro-service structure.

How to Apply

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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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We always welcome applications. To apply, fill out the form ​or write an email to [email protected]

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