Compose a brief and impactful cover letter based on the provided job description, incorporating the provided resume, mimicking the pairwise job description and cover letter examples. The letter generally should has five paragraphs, structed in the job you want to apply, motivation to do a PhD, motivation for this particular position, relevant work to this job, and profile. The letter should be written in a professional, yet conversational tone. Avoid using any placeholders, and ensure that the letter flows naturally and is tailored to the job.

Here is a guideline how to write each paragraph: The first paragraph should be as simple as possible, with the jobs name. The second paragraph should be written in a general way, why you want to do a PhD. The third paragraph should be tailored to the job, for example, applciations of your expertise or aligned research interests. The fourth paragraph should match the description of projects or achievenemnts in the provided resume with the job description, through topics vision-language models, open-set learning, limited label learning, or incremental learning. In the five paragraph, you should remain the paragraph provided in example cover letter and add additional details if you think it is useful.

Please provide only the text of the cover letter.

## Job Description:
```
Your job
We are looking for a PhD and a postdoctoral researcher to work within the multi-partner HAICu NWA-ORC project. This vacancy is for the PhD position, the Postdoc position is being advertised simultaneously: Postdoc Position on Multimedia Analysis in the HAICu Project. There are two research topics tackled in parallel for this project (see description below). Based on the applications, the topics will be assigned at PhD or Postdoc level. Both researchers will collaborate within the project.

This project is implemented by an ambitious consortium including many universities, knowledge institutions, archives, foundations, cultural institutions and business partners in the Netherlands. It aims to use improved access to digital heritage to tutor the Digital Citizen in the use of big data. It brings together AI researchers and Digital Humanities scholars to seek solutions to the problem of inadequate data-mining tools we have, aiming to derive information from the continuous stream of data about the present and the past. This will help citizens and other regular users, heritage curators and journalists who are interested in tapping heritage collections, as well as civic organizations and authorities interested in improving civic participation.

Research topic targets visual and multimodal feature learning for news ecosystems, analysing the complex multidimensional feature space of visual information to support data-driven journalism. This includes experiments for accountability, transparency, inclusiveness, and misinformation. The key technology is multimodal deep learning, and its extensions for these additional targets.

As a PhD candidate in this project, you will conduct research under the supervision of a promotor and co-promotor. This position offers you the opportunity to further develop your academic skills and contribute to groundbreaking research. Next to that it provides a unique opportunity to network with an extensive scientific and cultural network of people in the Netherlands, and to seek answers to societal problems jointly with a great team of top scholars.

Your qualities
We are looking for a talented PhD candidate, with a keen interest in academic and professional development, who meets multiple of the following requirements:

a (Research) Master’s degree in computer science or artificial intelligence;
demonstrable machine learning skills;
curiosity about societal issues and how AI can be employed for the benefit of the public;
strong teamwork skills, as collaboration with other researchers and stakeholders is key to the project’s success;
comfortable in doing multidisciplinary research and interacting with important stakeholders and eager to conduct innovative and thorough research;
excellent writing skills in English.
```
## My resume:
```
personal_information=PersonalInformation(name='Federico', surname='Elia', date_of_birth='10/04/2001', country='Ireland', city='Dublin', address='Dublin 1', zip_code='520123', phone_prefix='+353', phone='892338335', <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>', github=Url('https://github.com/feder-cr'), linkedin=Url(<PRESIDIO_ANONYMIZED_URL>)) education_details=[EducationDetails(education_level='Master of Engineering', institution='A Institute', field_of_study='Communication and Information System', final_evaluation_grade='4/4', start_date='2020', year_of_completion=2023, exam=None), EducationDetails(education_level='Bachelor of Engineering', institution='B University', field_of_study='Software Engineering', final_evaluation_grade='3.56/4', start_date='2016', year_of_completion=2020, exam=[{'C Language Programming': '4'}, {'Object-Oriented Visual Programming': '4'}, {'Web Programmin': '4'}, {'Object-Oriented Programming (Java)': '4'}, {'Computer Vision Technology': '4'}, {'.NET Software Development Architectur': '4'}, {'Mobile Application Development': '4'}, {'Software Interaction Design and Presentation': '4'}, {'Operating System Principles': '4'}, {'Database Principle and Application': '4'}])] experience_details=None projects=[Project(name='Prompt-Guided Consistency Learning for Multi-Label Classification with Incomplete Labels', description='Under review at journal Neural Networks, 2024. Abstract: Addressing insufficient supervision and improving model generalization are essential for multi-label classification with incomplete annotations, i.e., partial and single positive labels. Recent studies incorporate pseudo-labels to offer additional supervision for the model and enhance its generalization capabilities. However, the noise in the pseudo-labels self-generated by the model keeps accumulating, leading to confirmation bias in the training process. As an effective approach for few-label learning, consistency regularization tries to prevent the accumulation of pseudo-label noise through data-augmented diversity, but it is based on model predictions only to provide limited mitigation of confirmation bias. To further mitigate confirmation bias, we build a prompt-guide consistency learning (PGCL) framework for two incomplete labeling settings. Specifically, we propose intra-category supervised contrastive loss to impose the consistency constraint in per-category feature space, instead of spanning across the feature spaces of all categories as in traditional inter-category supervised contrastive loss. Besides, we design a class-specific semantic decoupling module, leveraging the strong vision-language alignment capability of CLIP, since the proposed contrastive loss requires high-quality label-level representations as contrastive samples. Extensive experimental results on multiple datasets show that our method can effectively solve the problem of two incomplete labeling settings and achieve state-of-the-art performance.', link=None), Project(name='A Benchmark and Baseline for Open-Set Incremental Object Detection', description='Published at International Joint Conference on Neural Networks (IJCNN), 2024. Abstract: In real-world applications, detectors are expected to evolve and improve their perceptual abilities through incremental learning of the unknown. Open world object detection (OWOD) simulates this process by recognizing unknown objects and incrementally learning these unknown classes as labeled data becomes available. However, two issues exist in the training images of OWOD datasets: instances of both 1) unknown classes and 2) previously known classes are present. To solve them, this paper proposes Open-Set Incremental Object Detection (OSIOD), which is defined as two processes: learning from the known, and an infinite cyclical process consisting of detection of the known and the unknown, together with incremental learning of the unknown. We construct a benchmark dataset by filtering out images containing unknown and/or previously known instances, which can also be used to create a new incremental object detection scenario where images with only new classes are used for incremental learning. Additionally, we propose a baseline method for OSIOD by introducing label smoothing and fine-tuning for unknown class discovery and catastrophic forgetting, respectively. Comprehensive experiments provide insights into the baseline. We hope that our benchmark, baseline, and insights will promote research towards OSIOD.', link=None), Project(name='A Simple Unknown-Instance-Aware Framework for Open-Set Object Detection', description='Published at international Conference on Information Science and Technology (ICIST), 2023. Abstract: Open-set object detection aims to detect training set objects (known classes) and non-training set objects (unknown classes). The current frameworks rely on human-in-the-loop, i.e., humans provide training data of unknown classes, to be aware of unknown-instance. However, it is difficult to collect unknown-class images in the real world. In this work, we propose a simple unknown-instance-aware framework for open-set object detection, which can extend close-set detectors to open-set object detection. The proposed framework consists of a class-agnostic proposal module and a prompt-guided semantic module. The class-agnostic proposal module generates region proposals, which are used by the prompt-guided semantic module. The prompt-guided semantic module distinguishes unknown instances by the similarity score between embeddings and image features, which is based on the aligned image-text space of pre-trained vision-language models, e.g., CLIP. The experiments show the class-agnostic proposal module is better than the class-aware locator in localization performance, and the prompt-guided semantic module has better unknown-class recognition performance.', link=None), Project(name='Saliency Regularization for Self-Training with Partial Annotations', description='Published at nternational Conference on Computer Vision (ICCV), 2023. Abstract: Partially annotated images are easy to obtain in multilabel classification. However, unknown labels in partially annotated images exacerbate the positive-negative imbalance inherent in multi-label classification, which affects supervised learning of known labels. Most current methods require sufficient image annotations, and do not focus on the imbalance of the labels in the supervised training phase. In this paper, we propose saliency regularization (SR) for a novel self-training framework. In particular, we model saliency on the class-specific maps, and strengthen the saliency of object regions corresponding to the present labels. Besides, we introduce consistency regularization to mine unlabeled information to complement unknown labels with the help of SR. It is verified to alleviate the negative dominance caused by the imbalance, and achieve state-ofthe-art performance on Pascal VOC 2007, MS-COCO, VG200, and OpenImages V3.', link=None), Project(name='Coarse-to-Fine Incremental Few-Shot Learning', description='Published at European Conference on Computer Vision (ECCV), 2022. Abstract: Different from fine-tuning models pre-trained on a large-scale dataset of preset classes, class-incremental learning (CIL) aims to recognize novel classes over time without forgetting pre-trained classes. However, a given model will be challenged by test images with finer-grained classes, e.g., a basenji is at most recognized as a dog. Such images form a new training set (i.e., support set) so that the incremental model is hoped to recognize a basenji (i.e., query) as a basenji next time. This paper formulates such a hybrid natural problem of coarse-to-fine few-shot (C2FS) recognition as a CIL problem named C2FSCIL, and proposes a simple, effective, and theoretically-sound strategy Knowe: to learn, freeze, and normalize a classifier’s weights from fine labels, once learning an embedding space contrastively from coarse labels. Besides, as CIL aims at a stability-plasticity balance, new overall performance metrics are proposed. In that sense, on CIFAR-100, BREEDS, and tieredImageNet, Knowe outperforms all recent relevant CIL or FSCIL methods.', link=None)] achievements=[Achievement(name='Excellence award of the 2nd A Competition', description='Texts as images to tuning the vision-language model CLIP for zero-shot multi-label classification'), Achievement(name='Third place of the 4th B Challenge', description='Open-vocabulary object detection using image-level supervision in addition to the detection data'), Achievement(name='Excellence award of the 3rd C Competition', description='Multiple object tracking based on YOLOv5 and SORT')] certifications=None languages=None interests=None
```
## Example job Description:
```
We are looking for a highly motivated and ambitious PhD candidate to join our team and contribute to cuttingedge research in machine learning and deep learning. This is a fully funded PhD position within the Division of Computational Science and Technology at KTH. Successful candidates will also become part of the SciLifeLab community, Sweden’s leading research infrastructure for life sciences.
As a PhD student, you will work on developing innovative machine-learning and deep-learning frameworks to enhance our understanding and prediction of drug interactions and adverse reactions using diverse data types, including Sweden's comprehensive drug interaction dataset. Your work will focus on the following areas:
Graph Convolutional Networks
Graph Embedding
Multimodal Learning
If you are passionate about advancing AI in pharmacology and eager to work in a collaborative and interdisciplinary environment, we encourage you to apply and join us in addressing these exciting challenges.

```
## Example cover letter:
```
I am writing with great enthusiasm to express my interest in this position - Machine Learning for Drug Interaction Prediction. \\

\textbf{Motivation}. I am driven by a passion for exploring the frontier, facing challenges, and overcoming obstacles. I enjoy the sense of achievement and self-worth that the final output brings me, especially after going through numerous difficulties. Moreover, undertaking a PhD will further enhance my abilities, including but not limited to problem identification, analysis, and resolution, which will provide a solid foundation for my career. It enables me to develop technology that can help people in the real world, which is my ambition. I was, am, and will devote myself to pushing the boundaries of human knowledge while focusing on reality-based research. \\

\textbf{Motivation for this particular position}. This position aligns with my passion, as it offers me a genuine opportunity to apply deep learning technologies in a realistic setting and has the potential to assist researchers. Furthermore, I am interested in developing models that can synthesise information from different modalities, leading to more accurate predictions and a deeper understanding of complex biological phenomena. It resonates with my technical skills and experience of exploring the fusion of images and texts. \\

\textbf{Relevant work}. My relevant work is primarily on multimodal learning, especially my work with the contrastive language-image pre-training (CLIP) model. Like CLIP learns to associate images with textual descriptions, similar techniques could be applied to correlate chemical structures, clinical trial results, and genomic data, offering a more holistic view of drug interaction. By aligning different modalities, we can create a more comprehensive dataset that captures the complexity of drug interactions. Besides, CLIP's cross-modal alignment capabilities can be used to align textual data from clinical notes and research papers with visual data from drug compound images and cellular response images. This alignment can reveal correlations and patterns that are not evident when analyzing data in isolation. \\

\textbf{Profile}. With a background in computer science, I have excellent problem-solving and programming skills, as well as in-depth knowledge of deep learning. On top of that, I have published several papers at top computer vision conferences, including ICCV and ECCV. I have experience of working on papers independently and collaboratively, in a variety of roles including leader, participant, writer, experimenter, and proofreader. In particular, I have led the experiments in all the projects I have participated in. The largest dataset I have dealt with is Open Images V3 with ~3.4 million training images. I used distributed data parallel (DDP) on 8 GPUs to accelerate the training process. Besides, I am unifying the pipeline parallelism with the DDP on ~5.2 million training images from Open Images V4, as the model now developed cannot fit on one GPU in the ongoing project. In conclusion, my background in programming, knowledge of computer science, and experience in computer vision allow me to adapt quickly and move the project forward. All of this makes me an excellent candidate.

```