Student opportunities
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
Kick start your career with us!
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Develop professional skills, practice time management, and improve your problem solving skills under the guidance of expert coaches.
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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.
Data Science
Bio-inspired optimization and rule-based control on transport scheduling optimization (MSc)
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
Decision Support System for semi-autonomous vehicle based on Explainable AI (BSc/MSc)
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
Efficient techniques for the analysis of directed graphs: applications in logistics (MSc)
The distribution networks of big suppliers could be seen as very complex graph structure where each node corresponds to a production plant, a distribution center, a warehouse, or a final customer and each link corresponds to a connection (lane) between two locations. Optimizing the logistics means above all optimizing such a complex network in order to find the best routes and the best schedules for the transportation of goods. Unfortunately, finding the optimal solution is an intrinsically complex problem from a computational point of view (cf. the traveling salesman problem). During this internship/thesis, the student will analyze existing methods for treating directed graphs and apply then to some real-world datasets.
How to Apply
Evaluating the stability of time-dependent set of rules (BSc/MSc)
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
Improving the performances of rule-based machine learning methods through a sequential ensemble approach (BSc/Msc)
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
Recurrent Neural Networks for the elaboration of Natural Language (MSc)
Understanding the contents of written documents, including the contextual nuances, is a very challenging task and involves linguistics, computer science and artificial intelligence. Recently, the broad diffusion of efficient deep learning models has allowed to develop new algorithm for NLP which are based on the analysis of big text corpora to extract semantical and logical relationships within a text. In particular, recurrent neural networks have become quite popular since they allow to consider not only the single words but also their sequence within each sentence. During this internship/thesis a RNN model will be implemented and tested starting from publicly available corpora.
How to Apply
Rule-based methods for unsupervised detection of anomalies (MSc)
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
Rule-based robotic data correction (MSc)
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
Unsupervised algorithms for converting Natural Language (NL) into vector representations (MSc)
Natural language is intrinsically unstructured; for this reason, to make elaborations and inferences from it, it must be converted into a structured format. Different feature extraction techniques have been proposed to transform a text into a vector of numbers, including methods that analyze big amount of historical data to infer word-word co-occurrences allowing to build a model for vector representation. During this internship/thesis, these models will be explored and applied to some real-world datasets.
How to Apply
DevOps
Piece-wise linear regression algorithm (MSc)
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
NoSQL DB implementation for big data analysis (MSc)
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
Machine Learning for Load balancing in real-time computation (MSc)
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
Grid Representation for general advanced type (BSc)
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
AMQP broker optimization in distributed system (BSc)
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
Economics
Machine Learning in Balance Sheet Analysis (MSc)
Many decisions, both individual (trading) and organizational (resource allocation) are driven by a careful analysis of balance sheet data. Together with classical techniques, which involve the computation of some well-known indices, also machine learning approaches, which allow a richer insight on the situation, are growing in popularity. The candidate will investigate available ML balance sheet analysis techniques and will have the chance to develop an analysis pipeline of this kind without the need to write code, using the visual programming techniques of Rulex Platform.
How to Apply
Marketing/Communications
Internal Communication Thesis Intern
Rulex is a leading technology company specialized in no-code software for data preparation, machine learning, and optimization.
Our mission is to help people and organizations take the best possible decisions by seamlessly combining transparent data-driven knowledge with human expertise. We have worked hard to build a unique platform that can be used by anyone from a standard laptop, without writing a line of code. Since 2014, our team, together with an extensive network of partners, has helped clients from all over the world improve operational and decision-making processes by using data-driven insight.
Our ultimate goal is to build a world where data work for you. A world where modern technology makes life easier, by helping people face everyday problems and long-term challenges.
Our project
Rulex is looking for a new motivated and talented Internal Communication Intern to join our team. If you are looking for an inclusive, smart, and empowering working environment, Rulex is the right place for you. You will be working with genuine people, surrounded by a learning-oriented culture, where you can develop your skills and grow personally and professionally.
Rulex: people, data, decisions. An enthusiastic and young group of professionals: collaboration is our everyday strategy to produce great products and tackle new challenges. We encourage a research-focused environment to further people’s professional career and improve personal growth.
Duties
The student is expected to:
- Analyse best practices for internal communication management
- Define an internal communication strategy with the HR, Academy and Marketing Communications Teams
- Support the development of internal communication projects and tools
- Provide support during the development of internal events
This internship opportunity is aimed to be channelled into the creation of a thesis project.
Skills
- High level of computer literacy in at least Microsoft tools
- English B2
- Excellent team collaboration/coordination skills, ability to organize work independently and respect deadlines
- Technical aptitude
- Precision
- Interest and curiosity in innovative technologies is essential
Requirements and Qualifications
- Currently undertaking a Master’s degree or Degree in Marketing related subjects, languages and translation, Human
Resources, Media Studies.
Employment location
Flexible Location
HQ: Rulex Innovation Labs
Genoa, Italy
How to Apply
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We always welcome applications. To apply, fill out the form or write an email to [email protected]